Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Tracking Multiple People with a Multi-Camera System
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We propose a multi-camera method to track several persons using constraints from the epipolar and projective geometries. The method is very accurate, fast, and simple. We first compute accumulator images for each time frame that shows the probability of object positions on the ground. We developed a voting based method that allows employment of the integral images to make the accumulator computation very fast. Next, we perform two-pass 3D tracking on the volume generated by stacking these accumulator images. Our main contributions are the fast computation of the accumulator images and application of fast 3D tracking methods like the Kalman Smoother instead of the computationally expensive methods like the Viterbi algorithm. The proposed tracking method is evaluated on people videos captured using four synchronized cameras.