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    International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-

    6367(Print), ISSN 0976 6375(Online) Volume 4, Issue 3, May June (2013), IAEME

    32

    SCHEMATIC MODEL FOR ANALYZING MOBILITY AND

    DETECTION OF MULTIPLE OBJECT ON TRAFFIC SCENE

    Pushpa D.

    1

    , Dr. H.S. Sheshadri

    2

    1Asst. Prof: Dept of Information Science & Engg, Maharaja Institute of Technology,

    Mysore, India2Prof.: Dept. of Electronics and communication Engg, PES College of Engg,

    Mandya, India

    ABSTRACT

    Detection, counting as well as gathering features to perform analysis of behavior

    of natural scene is one of the complex processes to be design. My previous work hasintroduced a simple model for identifying and counting the moving objects using simple

    low level features. The current work is an extension of the previous work where the focus

    is not only to detect and count the multiple moving objects but also to understand the

    crowd behavior as well as exponentially reduce the issues of inter-object occlusion. The

    image frame sequence is considered as input for the proposed schematic model.

    Unscented Kalman filter is used for understanding the behavior of the scene as well as for

    increasing the detection accuracy and reducing the false positives. Designed on Matlabenvironment, the result shows highly accurate detection rate.

    Keywords: Background Image, Foreground Image, Kalman Filter, Multiple Object

    Detection, Object tracking, Object counting.

    I. INTRODUCTIONThe area of computer vision is still in its infancy. It has many real-world

    applications, and many breakthroughs are yet to be made. Most of the companies in

    existence today that have products based on computer vision can be divided into three

    main categories: auto manufacturing, computer circuit manufacturing, and face

    INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING

    & TECHNOLOGY (IJCET)ISSN 0976 6367(Print)ISSN 0976 6375(Online)

    Volume 4, Issue 3, May-June (2013), pp. 32-49 IAEME:www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI)

    www.jifactor.com

    IJCET

    I A E M E

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    recognition. There are other smaller categories of this field that are beginning to be

    developed in industry such as pharmaceutical manufacturing applications and traffic

    control. Auto manufacturing employs computer vision through the use of robots that put

    the cars together. Computer circuit manufacturers use computer vision to visually checkcircuits in a production line against a working template of that circuit. Computer vision is

    used as quality control in this case. The third most common application of computer

    vision is in face recognition. This field has become popular in the last few years with the

    advent of more sophisticated and accurate methods of facial recognition. Traffic control is

    also of interest because computer vision software systems can be applied to already

    existing hardware in this field. By traffic control, we mean the regulation or overview of

    motor traffic by means of the already existing and functioning array of police monitoring

    equipment. Cameras are already present at busy intersections, highways, and other

    junctions for the purposes of regulating traffic, spotting problems, and enforcing laws

    such as running red lights. Computer vision could be used to make all of these tasks

    automatic. In the last few years, object detection and localization have become very

    popular areas of research in computer vision. Although most of the categorization

    algorithms tend to use local features, there is much more variety on the classification

    methods. In some cases, the generative models show significant robustness with respectto partial occlusion and viewpoint changes and can tolerate considerable intra-class

    variation of object appearance [1, 2, 3]. However, if object classes share a high visual

    similarity then the generative models tend to produce a significant number of false

    positives. On the other hand, the discriminative models permit us to construct flexible

    decision boundaries, resulting in classification performance often superior to thoseobtained by only generative models [4, 5]. However, they contain no localization

    component and require accurate localization in positions and scale. In the literature, the

    standard solution to this problem is to perform an exhaustive search over all position and

    scales. However, this exhaustive search imposes two main constraints. One of them is thedetectors computational complexity. It requires large computational time for relatively

    large number of objects. The second is the detectors discriminance, since a large number

    of potential false positives need to be excluded.

    Problems like inter-object occlusion, different object moving at all different speed,

    walking in arbitrary direction etc. will pose a huge challenge in the development as well

    as design of multiple object detection as well as tracking from the crowded scene.

    Majority of the video monitoring techniques are normally experimented with permanent

    image capturing device which is comparatively unproblematic in terms of real time

    application in order to accomplish image capturing parameters. Therefore, the current

    research work has been experimented with monocular adjusted image capturing device,

    which facilitates to design a distinctive connection between the subjects walking in realtime and image plane. The phenomenon is considered for isometric view of the subjects

    walking. In Section 2, we review recent work on multi-object detection. In Section 3,

    problem description is discussed in brief followed by proposed system in section 4.

    Section 5 discusses about algorithm design that is implemented in the proposed system

    which is followed by Section 6 that discusses about result discussion. Finally concludes

    by summarizing the entire research work in section 7.

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    II. RELATEDWORKMoving object detection is the basic step for further analysis of video. Every

    tracking method requires an object detection mechanism either in every frame or whenthe object first appears in the video. It handles segmentation of moving objects from

    stationary background objects. This focuses on higher level processing .It also decreases

    computation time. Due to environmental conditions like illumination changes, shadow

    object segmentation becomes difficult and significant problem. A common approach for

    object detection is to use information in a single frame. However, some object detection

    methods make use of the temporal information computed from a sequence of frames to

    reduce the number of false detections. This temporal information is usually in the form of

    frame differencing, which highlights regions that changes dynamically in consecutive

    frames. A brief survey on the past research work is described here. One of the

    significance work carried out by Koller et al. [6][7] is design of an algorithm which uses

    offline calibrated camera step to aid the recovery of the 3D images, and it is also passed

    through Kalman Filter to update estimates like location and position of the object. The

    concept of Bayesian technique for image segmentation based on feature distribution is

    also deployed where a statistical mixture model for probabilistic grouping of distributeddata is [8] adopted. It is mainly used for unsupervised segmentation of textured images

    based on local distributions of Gabor coefficients. Toufiq P. et al., in [9] describes

    background subtraction as the widely used paradigm for detection of moving objects in

    videos taken from static camera which has a very wide range of applications.

    Polana and Nelson [10] has adopted spatial-temporal discretization which isallocated to every region of image, accomplished normally by overlapping a grid on the

    image and its mean visual stream. The episodic movement prototype is estimated by

    performing Fourier analysis on the resulting attribute vector. The use of movement field

    by Shio and Sklansky [11] has shown the accomplishment by correlation technique overconsecutive image frames, which results in identification of movement regions with

    uniform direction when smoothening is done along with quantization of image regions.

    Deployment of clusters of pixels has been done by Heisele et al. [12] to be attained

    by different grouping methods as fundamental constituents for tracking. The grouping

    analysis is performed by both spatial data and color information. The concept behind this

    is that appending spatial data generates grouping more steady than deploying only color

    data. There has been also abundant use of k-means algorithm while working on clustering

    techniques as evident in Cutler and Davis [13] for designing a change detection

    algorithm. The prototype also facilitates a map of the image elements signifying moving

    subjects along with precise thresholding.

    Use of wavelets transform has been seen in the work of Papageorgiou and Poggio[14] for characterizing a moving object shape and then examines the transform technique

    of the image to evaluate the movement patterns. Implementation of two dimensional

    contour shape analyses is seen in the work of Wren et al. [15] which tends to recognize

    different parts of body of the moving subjects using heuristics techniques. Cai and

    Aggarwal [16] proposed a model with a uncomplicated head-trunk replica to trail humans

    across multiple image capturing devices. Beymer and Konolige [17] have worked for

    fitting a trouble-free shape on the candidate image.

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    Dimitrios Makris [18] with the non-trivial problem of performance evaluation of

    motion tracking. We propose a rich set of metrics to assess different aspects of

    performance of motion tracking. We use six different video sequences that represent a

    variety of challenges to illustrate the practical value of the proposed metrics by evaluatingand comparing two motion tracking algorithms. Max e.t. al [19] describes performance

    results from a real-time system for detecting, localizing, and tracking pedestrians from a

    moving vehicle. It achieves results comparable with alternative approaches with other

    sensors, but offers the potential for long-term scalability to higher spatial resolution,

    smaller size, and lower cost than other sensors. But issue of this work is it cannot segment

    people or objects in close contact. Jifeng [20] has presented a novel object tracking

    algorithm where the author has used the joint color texture histogram to represent a target

    and then applying it to the mean shift framework.

    To analyze images and extract high level information, image enhancement, motion

    detection, object tracking and behavior understanding researches have been studied. In

    this section, we have studied and presented different methods of moving object detection

    from prior research work. Various methods like background subtraction, temporal

    differencing, statistical methods were explored. Detection techniques into various

    categories, here, we also discuss the related issues, to the moving object detectiontechnique. The drawback of temporal differencing is that it fails to extract all relevant

    pixels of a foreground object especially when the object has uniform texture or moves

    slowly. When a foreground object stops moving, temporal differencing method fails in

    detecting a change between consecutive frames and loses the track of the object. The

    survey also identified background subtraction method in deep because of itscomputational effectiveness and accuracy. This section gives valuable insight into this

    important research topic and encourages the new research in the area of moving object

    detection as well as in the field of computer vision. It was also found that research on

    object tracking can be classified as point tracking, kernel tracking and contour trackingaccording to the representation method of a target object. In point tracking approach,

    statistical filtering method has been used to estimating the state of target object. Kalman

    filter and particle filter are the most popular filtering method. In kernel tracking approach,

    various estimating methods are used to find corresponding region to target object. Now a

    day, the most preferred and popular kernel tracking techniques are based on mean-shift

    tracking and particle filter. Contour tracking can be divided into state space method and

    energy function minimization method according to the way of evolving of contours. The

    survey has witnessed various publications that are focused on addressing issues on object

    detection. Finally, Table 1 will show the evidence of some recent publications related to

    the issue of Object detection. Majority of the Journals based on moving objects are

    selected for the review purpose as shown below:

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    Table 1 Existing Approaches used in Moving Object Detection

    Year Authors Problem Focused Techniques Used Remark

    2010

    Djamel, Nicolas [21] Counting people Skelton graph,

    Background subtraction

    Objects other than human

    are not consideredConte e.t. al [22] Counting people Motion vector,

    Support vector Regressor

    Objects other than human

    are not considered

    Burkert e.t. al [23] Monitoring people based

    on aerial images

    Microscopic

    & Mesoscopic parameter,

    trajectory

    Inter-object Occlusion not

    focused on

    Conte e.t. al [24] Counting Moving

    People

    Detection using interesting

    points

    Objects other than human

    are not considered

    Widhalm [25] Flow of Pedestrian Growing Neural Gas,

    Dynamic Time Warping

    Objects other than human

    are not considered, Inter-

    object Occlusion not

    focused on

    2011

    Dehghan e.t. al [26] Automatic Detection &

    Tracking of Pedestrians

    Static Floor Field,

    Boundary Floor Field,

    Dynamic Floor Field

    Addressed Occlusion, good

    results obtained, not

    focused on multiple objectsmoving

    Spampinato [27] Event Detection in

    crowd

    Lagrangian Particle

    Dynamics

    Results found with Issue in

    noises in frames affecting

    the performance of flow

    map estimation

    Jae Kyu Suhr [28] Moving Object

    Detection

    Background compensation

    method using 1-D feature

    matching & outlier

    rejection

    Not focused on inter-object

    occlusion, Multiple objects

    are not considered

    Lei, Su, Peng [29] Maritime

    Surveillance

    Trajectory Pattern Mining Better result but cannot be

    used in crowded scenes

    Sirmacek [30] Tracking people from

    airborne images

    Kalman filter Not focused on inter-object

    occlusion, Multiple objects

    are not considered

    Year Authors Problem Focused Techniques Used Remarks

    2012

    Cui, Zheng [31] Moving Object

    Detection

    Graph cuts Not focused on inter-object

    occlusion, Multiple objects

    are not considered

    Chen e.t. al [32] Counting People in

    crowd

    2-Stage Segmentation Better result, but impact of

    occlusion & multiple

    objects are not considered

    Gowsikhaa [33] Human Activity

    Detection

    Artificial Neural Network Topic is narrowed down to

    face recognition system,

    Occlusion issue is not

    addressed

    Rosswog [34] Object detection in

    crowd

    SVM Occlusion issue is not

    addressed

    Arif [35] Counting Moving Object Neural Network Illumination state &

    Occlusion issues are not

    addressed

    Although, we cant conclude that the previous work has got any demerits or

    disadvantages, but it can be seen that with each discoveries of research, there is a new idea

    which keeps the research on ongoing pace. But, in nutshell, it can be said that majority of the

    work has not much integrated the process of Multiple (different) objects detection along with

    solution for inter-object occlusion and reduction of false positives. Hence a research gap can

    be explored in this viewpoint.

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    III. PROBLEMDESCRIPTIONSearching for a known object in a specified scene and locating a given object are

    inherently different problems. Object recognition is a computationally expensive process.Fast algorithms are essential at all stages of the recognition process. Object recognition is

    tricky because a combination of factors must be considered to identify objects. These factors

    may include limitations on allowable shapes, the semantics of the scene context, and the

    information present in the image itself. Given a video clip, the initial problem is segregating

    it into number of frames. Each frame is then considered as an independent image, which is in

    RGB format and is converted into Gray scale image. Next the difference between the frames

    at certain intervals is computed. This interval can be decided based on the motion of moving

    object in a video sequence. If the object is moving quite fast, then the difference between

    every successive frame has to be considered, posing a difficulty in designing smart object

    identification system.

    The key issue of multiple targets tracking in the image sequences is the occlusion

    problem. During the occlusion time, unpredictable events can be activated. Thus, the trackingfailure and missed tracking often happened. To cope with this problem, a behavior analysis of

    multiple targets is required the first time. A specific target enters into the specific field of

    view, and then it is moving, stopping, interacting with other targets. And finally, it leaves the

    field of view. We can classify the behaviors of multiple targets accordingly as

    1. A specific target enters into the scene.2. Multiple targets enter into the scene.3. A specific target is moving and forms a group with other targets, or just moves beside

    other targets or obstacles.

    4. A specific target within the group leaves a group.5. A specific target continues to move alone, or stops moving and then starts to move

    again.

    6. Multiple targets in a group continue to move and interact between them, or stopinteracting and then start to move again.

    7. A specific target leaves a scene.8. A specific group leaves a scene

    The events of (1), (4), (5), and (7) can be tracked using general tracking algorithms.

    However, the events of (2), (3), (6) and (8) cannot be tracked reliably. Thus, to resolve this

    problem, we propose the occlusion reasoning method that detects occlusion activity status

    using Kalman Filter.

    IV. PROPOSEDSYSTEMThe prime aim of the proposed work is to design a robust framework that is capable of

    identifying mobile objects using sequences of frames captured from video. The research workis designed not only for its utility in crowd detection that was presented in my previous work

    [36] but now the proposed framework will be used for extensive analysis of the video dataset.

    Various knowledge and data about the coordinates and characteristics of the mobile object at

    various instance of time as the fundamental of identifying abnormal mobile object detection

    pertaining to its movement. The flow of the proposed system is as highlighted in Fig.1.

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    Figure 1 Process Flow of the proposed system

    The proposed study is designed to accept the sequential frames of the video, which is

    converted to blob image or binary images, Image plane is defined for the purposed of frame

    representation as well as for the purpose of estimating the centroid location.(we are using the

    relative distance from centroid in image plane from the position of foreground object). A

    Gaussian distribution is applied to understand the pixel density, so that distinction of every

    pixel changes is easy to find/estimate. Another purpose of Gaussian distribution is to find the

    peak in pixel intensity distribution which will be used for threshold estimation. The white

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    pixels are compared with the threshold in order to distinguish the foreground and background

    object. However, after estimating the relative position of newly detected foreground object,

    background is also found and finally state transition matrix (STm) is compared with the

    threshold value (Tm). The condition is checked to predict distinctly about the foreground andbackground object. The Kalman filter is the best part of the contribution.

    The proposed system uses the frames captured from the still camera, where the

    visibility is restricted to a defined area of focus of the aperture of the camera. This method is

    adopted to analyze the preciseness of the mobility of the object, which could not be done on

    mobile video sensors. The system uses distinctive technique to filter out the foreground

    objects from the background for better accuracy. Basically, the background model is

    considered as prime module as the experimental framework designs for every pixels of the

    captured frame as a distribution of the typical values considered by background module. The

    main concept behind this is the most frequent values of a pixel that are corresponding to the

    background metaphors. The system is designed in such a way that the iterative mobility in the

    background is also captured. That will mean that even a slightest displacement on any object

    on the considered visibility screen will also be captured.

    Figure 2 showing Original Scene, Mask

    Figure 3 showing Original Scene, Mask

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    The categorization of the pixel into background or foreground is designed

    depending on the collected module of background using outlier-detection. According to

    this design, the pixels with the intensity values sufficiently distinctive from the

    background distribution are indexed as foreground. The framework will also use gradualalteration in the illumination factor by spontaneously upgrading the background model of

    every pixel in the frames. The same phenomenon will also be applicable to the static

    objects in the considered scene. An example input image and the corresponding pixel

    labeling computed from a background model can be seen on the left where foreground

    pixels were labeled as white. The person crossing the street can be clearly seen in Fig.2

    and Fig.3. From Fig.2 and Fig.3, by lumping large connected foreground regions into

    blobs, foreground objects, such as the person seen crossing the street is identified.

    However, the left images also show that an additional foreground object was erroneously

    detected in the top right corner due to violent motion of the trees in the background. Such

    occasional misdetections are taken into account at later stages of the object detection

    algorithm.

    The consecutive stage will include a sequence of the object blobs together across

    various frames of the scene. In order to accomplish this task, various attributes like

    position, velocity, and size are used to represent every object block. Unscented Kalmanfilter is specifically used in this work for this purpose for determining the actual

    coordinates of the mobile object identified on the scene-frames. The main purpose of

    adopting this step is to truncate the possibility of noise from blob estimations which could

    possibly lead to generation of the false-positives values.

    V. ALGORITHMDESIGNThe proposed work is performed on 32-bit Windows OS. In order to smoothly run

    the heavy computation from large datasets of frames, it is highly essential to use highconfiguration system with min 2.20 GHz processor with Intel core-i3 along with 4 GB

    RAM size. The programming is done on Matlab environment due to its faster

    computation of complex problems. The image sequence datasets has been taken from

    [36].

    By deploying the computed indexing, foreground objects are identified, however,

    direct use of these blobs for tracking is sensitive to noise and fails to determine object

    identities at different times in a sequence. In order to couple a sequence of state

    measurements, that is, relevant blob properties such as the centroid, centroid velocity

    between consecutive frames, or size into a denoised state trajectory, The filter operates as

    a two-stage process switching between absorbing evidence from the most recent

    foreground blob and forming a predictive distribution for the next. In the context oftracking multiple objects simultaneously, the key difficulty lies in determining which

    Unscented Kalman filter should absorb which new state measurement. If this association

    task is not solved adequately then Unscented Kalman filters are corrupted with wrong

    information.

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    Algorithm: Detection of object mobility

    Input: Image sequence datasetsOutput: Appropriate detection of object movement in visualization

    START1 Load set of image sequence2 Returns data matrix

    3 Reading Indexed individual frames4 Reading RGB components of the scene5 Reading frame number

    6 Estimate the log of the Gaussian distribution for each point in x using and .7 x=matrix of column-wise data points.

    8 =column vector

    9 =Covariance matrix of respective size10 Estimate the log probability for each column in x-centered.

    11 Deploy other parameters:

    12 weight=matrix of mixing proportion

    13 =Covariance14 Active_Gaussian=components that were either matched or replaced.

    15 Background_Threshold=Threshold standard deviation for outlier detection on

    each Gaussian.16 K= Quantity of components used in the mixtures.

    17 Alpha=How fast for adapting the component weights

    18 Rho=How fast to adapt the component means and covariances19 Threshold_Deviation=Threshold used for finding matching versus unmatched

    components20 INIT_Var= Initial variance for newly placed components

    21 INIT_MixProp= Initial prior for newly placed components

    22 COMPONENT_THRESH: Filter out connected components of smaller size

    23 BACKGROUND_THRESH: Percentage of weight that must be accounted forby background models

    24 Design a helper function for structure of Kalman filtering

    25 Define Kalman State type

    26 Create best background image27 Computes a probability density on image coordinates pos28 Set the state transition matrix

    29 Update using position only

    30 Update using position and velocity

    31 Compute a cost matrix32 Computes the squared distance between the centroid predictions of a list of objects

    and a list of observed blob centroids.

    33 Compute a cost matrix34 Compute the square distance between Kalman filter predictions and

    observations35 Create foreground_map36 Binary matrix

    37 Define chromaticity coordinates

    38 Show visualization of object detection

    END

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    VI. RESULT DISCUSSIONIn order to accomplish the research work, the whole detection protocol was assessed

    on a number of different sequences in order to analyze its accurateness and weakness. Theprevious work [36] done is able to detect objects from crowded scene, but false positive are

    quite moderate in number. Hence, this paper will use the technique of foreground detection

    that can be used for extracting useful information under various distinctive conditions,

    particularly when the data stream is recoded in different color space. The cumulative results

    obtained are as below in Fig.3

    Figure 4 Detection window showing object detection

    The above Fig. 4 shows frame rates and movement of each object, along with its

    count. The interesting thing is that it also shows every minute movement even for shaking

    leaves of tree. Manual inspection of various detection results suggested that multiple object

    tracking using the devised association technique performs adequately in many situations.

    Figure 5 Result showing successful detection rate

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    Figure 6 Result showing successful detection of multiple-object and counting

    Figure 7 Result showing preciseness in multiple-object detection counting

    The assessment is done considering three case scenarios e.g. i) Successful detection

    rate of object: Fig.5 shows very effectively both truck and human along with the person

    riding bicycle. ii) Successful detection of multiple-object and counting: Fig.6 shows very

    distinctively the human as well as truck as a moving objects. iii) Preciseness in multiple-

    object detection counting: Fig.7 shows an accurate detection of a person riding bicycle, a

    person walking, and a person .walking along with pram. All the three objects are accurately

    shown and counted too. The above implemented result shows better detection rate for

    multiple object with precise bounding box over the object. It successfully counts the objects

    too. The previous model has some detection issue due to inter-occlusion. However, 95% of

    the inter-occlusion issue are overcome in this model with highly precise detection rate.

    VII. PERFORMANCE DISCUSSIONFor evaluating the performance of the application, the selected video clip in chosen to

    understand the various frames as mentioned in previous section. The classifications of the

    images are done and consecutively Machine is trained where the total number of classes has

    to be defined with class name. Finally object recognition module is executed. This video

    segmentation method was applied on three different video sequences one of which are

    depicted in Fig 8 and Fig 9, which represents original pictures, mask, and detected Object.

    Figure 8 showing Original Scene, Mask, Detected Object (First Sequence)

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    Figure 9 showing Original Scene, Mask, Detected Object (Second Sequence)

    For the first video sequence Figure 2 & 3 shows the original image, the background

    registered image, and image obtained after background subtracted, and finally shows the

    count of the detected objects. The same is repeated for the next video sequence.

    Figure 10 Object counting for moving objects

    The system is able to track and count most vehicles successfully. Although theaccuracy of vehicle detection was 100%, the average accuracy of counting vehicles was 99%

    (Fig 10). Each video sequences are tested with various types of object and also in complex

    background too. The application is successfully able to identify as well as track various

    pedestrians separately along with intersection. The application also successfully conducts

    object recognition (Fig 11). This is due to noise which causes detected objects to become too

    large or too small to be considered as a vehicle.

    Figure 11 Moving Object recognition for separated objects moving

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    Figure 12 Different Moving object recognition in one scene.

    However, two vehicles will persist to exist as a single vehicle if relative motionbetween them is small and in such cases the count of vehicles becomes incorrect.

    Figure 13 Performance Analysis for Accuracy

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    Figure 14 Performance Analysis for Object Identification

    Figure 15 Performance Analysis for Object Recognition.

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    Fig 13 represents the performance analysis of accuracy level considering all the three

    videos for evaluation. The accuracy rate, identification and recognition rate is better

    approximated to 100% respectively. Fig 14 shows the identification analysis which represents

    the better identification rate in all the AVI files. Fig 15 shows better and enhancedperformance in recognition for all types of moving object.

    VIII. CONCLUSIONThis paper has presented a novel and highly precise model of detecting multiple

    objects in real time street scene with highly uncertainty of the types of objects, their

    movements, and their count rates. The previous model presented has been addressed for

    object detection from crowded scene with better detection rate, but even some moderate false

    positives were also raised due to inter-occlusion and variances in illumination. However,

    using unscented Kalman filter such issues were overcome in this model. The scope of the

    application for this model is now much higher. If the previous model could be used to detect

    multiple object than this model can be used to analyzed the behavior of object in the real timescene. The simulation result shows highly precise detection rate. However, these will

    eventuality limits the specification of the model in terms of segregating the targeted object

    with not so important object. For example, due to proposed algorithm that has used both

    foreground and background model, the framework detects even the minor movement of

    leaves of trees. This property might be little unwanted when we want to design an application

    specific to analyze pedestrian or car or some major moving object visually that keeps highest

    importance in application. Hence, the next further work will be enhanced the model using

    homographic transformation

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