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An efficient sequential approach to tracking multiple objects through crowds for real-time intelligent CCTV systems.

Liyuan Li¹, Weimin Huang, Irene Yu-Hua Gu

¹Institute for InfocommResearch, Singapore 119613. lyli@i2r.a-star.edu.sg

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|September 12, 2008

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This study introduces a 2.5-D approach for real-time multi-object tracking in crowds, using a novel dominant color histogram (DCH) for efficiency and robustness. The method successfully tracks over 90% of objects in complex surveillance scenarios.

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Real-time intelligent video surveillance demands efficient and robust multi-object tracking algorithms.
Handling occlusions and varying illumination in crowded scenes remains a significant challenge.

Purpose of the Study:

To propose a novel 2.5-D approach for real-time multi-object tracking in crowded environments.
To develop an efficient and robust object model for improved tracking accuracy.

Main Methods:

Formulated tracking as a maximum a posteriori estimation problem, approximated via sequential assignment and location steps.
Introduced a novel dominant color histogram (DCH) as an efficient object model, robust to illumination changes.
Implemented sequential solutions for assignment (depth order, one-by-one assignment) and location (two-phase mean-shift).

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Main Results:

The dominant color histogram (DCH) requires fewer color components and demonstrates robustness to illumination variations.
The proposed 2.5-D tracking method achieved successful tracking of approximately 90% of objects in public datasets.
Experiments in crowded public environments showed good tracking performance in complex occlusion scenarios.

Conclusions:

The developed 2.5-D approach provides an efficient and robust solution for real-time multi-object tracking in complex surveillance.
The dominant color histogram (DCH) significantly enhances the performance and robustness of tracking algorithms.
The method is suitable for tracking multiple objects in real-world surveillance scenarios with significant occlusions.