Tracking Multiple People in a Multi-Camera Environment
Tracking People using Multiple Cameras
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Reliably tracking multiple people using ordinary cameras is challenging, mostly due to the severe occlusions that occur when many people are involved. We tackle this problem by using several cameras, observing the scene from different viewpoints.
To exploit the resulting images, we developed a people detection algorithm called POM and a Deep-Learning based improvement, that use a generative model and Bayesian reasoning to estimate people’s positions in each individual time frame.
Given these estimates we then rely on a global optimization method known as K-Shortest Path (KSP) to generate trajectories.
Combining these two algorithms let us track people reliably and in real-time in spite of very significant occlusions. This technology now forms the basis for a video-based commercial product that tracks team-sport players.
Results
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Our Deep-Occlusion Reasoning method, which combines discriminative and generative models, allows us to perform 3D tracking, even in very crowded scenes.
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This video shows our recent tracking results using global appearance constraints – Basketball sequence.
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This video shows our recent tracking results using global appearance constraints – Soccer sequence.
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This video shows our recent tracking results using global appearance constraints.
Technology Transfer
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The C++ source code for the K-shortest path multiple object tracker used to generate the results shown on this page is available upon request for academic purposes. More information is available on the Software KSP page of our web site.
The Multi-Tracked Paths (MTP) is another light weight implementation of the K shortest-paths algorithm for multi-target tracking, and is available under the GPL3 license.
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Field Guide to Northern Tree-related Microhabitats: Descriptions and size limits for their inventory in boreal and hemiboreal forests of Europe and North America