international journal of computer engineering & … · schematic model for analyzing mobility...

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

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

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SCHEMATIC MODEL FOR ANALYZING MOBILITY AND

DETECTION OF MULTIPLE OBJECT ON TRAFFIC SCENE

Pushpa D.1, Dr. H.S. Sheshadri2

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

Mysore, India 2Prof.: 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 has

introduced 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 Matlab

environment, the result shows highly accurate detection rate.

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

Detection, Object tracking, Object counting.

I. INTRODUCTION

The 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

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ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 3, May-June (2013), pp. 32-49 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com

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33

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 check

circuits 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 respect

to 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 those

obtained 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 the

detector’s computational complexity. It requires large computational time for relatively

large number of objects. The second is the detector’s 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 real

time 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. RELATED WORK

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

tracking method requires an object detection mechanism either in every frame or when

the 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 distributed

data 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 is

allocated 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 over

consecutive 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 evaluating

and 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 detection

technique. 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 its

computational 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 tracking

according 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 considered

Conte e.t. al [22] Counting people Motion vector,

Support vector Regressor


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


are not considered

Widhalm [25] Flow of Pedestrian Growing Neural Gas,

Dynamic Time Warping


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 objects

moving

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


are not considered

Year Authors Problem Focused Techniques Used Remarks

2012

Cui, Zheng [31] Moving Object

Detection

Graph cuts Not focused on inter-object


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 can’t 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. PROBLEM DESCRIPTION

Searching 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 tracking

failure 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 stop

interacting 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. PROPOSED SYSTEM

The 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 work

is 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 and

background 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 gradual

alteration 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 Kalman

filter 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. ALGORITHM DESIGN

The 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 high

configuration 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 of

tracking 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 datasets

Output: Appropriate detection of object movement in visualization

START 1 Load set of image sequence

2 Returns data matrix

3 Reading Indexed individual frames

4 Reading RGB components of the scene

5 Reading frame number

6 Estimate the log of the Gaussian distribution for each point in x using µ and σ.

7 x=matrix of column-wise data points.

8 µ =column vector

9 σ =Covariance matrix of respective size

10 Estimate the log probability for each column in x-centered.

11 Deploy other parameters:

12 weight=matrix of mixing proportion

13 σ =Covariance

14 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 covariances

19 Threshold_Deviation=Threshold used for finding matching versus unmatched

components

20 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 for

by background models

24 Design a helper function for structure of Kalman filtering

25 Define Kalman State type

26 Create best background image

27 Computes a β probability density on image coordinates pos

28 Set the state transition matrix

29 Update using position only

30 Update using position and velocity

31 Compute a cost matrix

32 Computes the squared distance between the centroid predictions of a list of objects

and a list of observed blob centroids.

33 Compute a cost matrix

34 Compute the square distance between Kalman filter predictions and

observations

35 Create foreground_map

36 Binary matrix

37 Define chromaticity coordinates

38 Show visualization of object detection

END



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VI. RESULT DISCUSSION

In 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. The

previous 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 DISCUSSION

For 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 the

accuracy 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 motion

between 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 enhanced

performance in recognition for all types of moving object.

VIII. CONCLUSION

This 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 time

scene. 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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