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Related Experiment Videos

An active vision system for multitarget surveillance in dynamic environments.

Ardevan Bakhtari¹, Beno Benhabib

¹Promation Engineering Ltd., Mississauga, ON L4W 2W5, Canada. bakhtar@mie.utoronto.ca

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

|February 7, 2007

Summary

This study introduces an agent-based system for dynamic camera control, improving surveillance by adjusting camera positions and orientations to avoid occlusions and capture optimal views of moving objects.

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Simultaneous surveillance of multiple objects with unknown trajectories in cluttered environments poses significant challenges.
Traditional sensing systems often lack the adaptability to dynamically reconfigure for optimal performance.

Purpose of the Study:

To present a novel agent-based method for dynamic, coordinated selection and positioning of active-vision cameras.
To enhance surveillance performance by maximizing coverage, avoiding occlusions, and acquiring preferred viewing angles.

Main Methods:

An agent-based approach for dynamic sensor selection and positioning.
Real-time adjustment of camera orientation and position to track multiple objects.
Verification through simulations and implementation on an experimental prototype for automated facial recognition.

Main Results:

The proposed system dynamically adjusts camera parameters to optimize surveillance.
Simulations and experimental results demonstrate improved system performance.
Effective online dispatching strategy enhances surveillance capabilities.

Conclusions:

Dynamic sensor reconfiguration significantly improves surveillance performance in complex environments.
The agent-based method offers a robust solution for real-time object tracking and surveillance.
The system is effective for applications like automated facial recognition.