Fast and Robust Algorithm of Tracking Multiple Moving Objectsfor Intelligent Video Surveillance

Systems

Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo,2011

Goal

• detecting and tracking multiple moving objects

• real-time detecting• robustness against the environmental

influences and the speed

Outline

• Introduction• Previous Methods• Detecting Moving Objects– Extraction of Moving Objects– Grouping Moving Objects

• Tracing Moving Objects • Implementation and Experiment• Conclusions

Introduction

• In the traditional systems that a person should always monitor video.

• intelligent video surveillance systems are high-cost and low-efficiency

• Environment affects a lot.• This paper propose a method detecting and

tracking multiple moving objects in real-time.

Previous Methods

• particle filter ,extended Kalman filter• Background modeling (BM) or the Gaussian

mixture model (GMM)

• gray-scale BM shows the image information is excessively attenuated.

Extraction of Moving Objects

• Using RGB color BM instead of gray-scale BM• Each pixels will compare with previous pixels

in little group.• If it is stationary, the pixels will be black.• The parameter δ is proposed to overcome the

sensitivity problem .• δ would be different on different camera.

Extraction of Moving Objects

Extraction of Moving Objects

Extraction of Moving Objects

Grouping Moving Objects

• The individual tracking of neighboring or overlapping objects requires a lot of computational capacity .

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

Grouping Moving Objects

• Contour Tracing

Grouping Moving Objects

• its initial search position is set to be d+2 (mod 8)

Tracing Moving Objects

Tracing Moving Objects

Implementation and Experiment

• The 33Mbit IP camera provides the input image with 704x480 pixels.

• The surveillance image is transmitted through Internet.

• 2.66GHz CPU and 4GB RAM PC for the image signal processing and the proposed algorithm.

Implementation and Experiment

Implementation and Experiment

Implementation and Experiment

Conclusions

• Real-time detecting and tracing• Only for fixed camera.• Future works can be on predicted position.