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Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems

Chairman: Dr. Hung-Chi YangPresenter: Fong-Ren SieAdvisor: Dr. Yen-Ting ChenDate: 2013.10.16

Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo,

IEEE Transactions on Consumer Electronics, Vol. 57, No. 3,

August, 2011

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OutlineIntroductionMethodologyResultsConclusionsReferences

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IntroductionThe traditional video surveillance

system◦Closed-circuit televisions (CCTV)◦Digital video recorders (DVR)

Disadvantages◦Need someone to monitor and

searchReal time intelligent video

surveillance systems◦High-cost and low-efficiency

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IntroductionThe intelligent video surveillance

system is a convergence technology◦Detecting and tracking objects◦Analyzing their movements◦Responding

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IntroductionTracking Multiple Moving Objects

for Intelligent Video Surveillance Systems◦The basic technologies of the

intelligent video surveillance systems.

◦To detect and track the specific moving objects.

◦Eliminate the environmental disturbances

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IntroductionEliminate the environmental

disturbances◦The Bayesian method such as the

Particle Filter(PF) or the Extended Kalman Filter (EKF)

◦Background modeling (BM) or the Gaussian mixture model (GMM).

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IntroductionRGB BM with a new sensitivity

parameter to extract moving regions

Morphology schemes to eliminate noises and labeling to group the moving objects.

MethodologyDETECTING MOVING OBJECTS

◦Extraction of Moving Objects BM involves the loss of image information

compared with the color BM using RGB and HSI color space models

◦Gray-scale BM Image information is excessively

attenuated.

◦RGB color model Very sensitive to even small changes

caused by light scattering or reflection.

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MethodologyGray-scale BM

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MethodologyRGB color model

Prevent excessive attenuationShorter execution time

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MethodologyBinary image

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MethodologyThe group tracking

◦Prevent the problems of the individual tracking

◦A grouping scheme is required to classify moving objects into several groups

◦The 4-directional blob labeling is employed to group moving objects

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Methodology

4-directional blob-labeling

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MethodologyTracking moving object

◦Predicting the position of each group◦Recognizing the homogeneity of

each group in the sequential frames◦identifying the newly appearing and

disappearing groups.

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Methodology

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Results

(d) The 169th frame

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Results

The error of the predicted position of each group

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Results

The processing time of the proposed method

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ConclusionsDetecting and tracking multiple

moving objects◦Can be applied to consumer electronics◦The robustness and the speed◦The robustness against the

environmental influences◦The high-speed of the image processing◦The method is intended for a fixed

camera

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Thank you for your attention