Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
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
Multi-camera people tracking by collaborative particle filters and principal axis-based integration
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Automatic resource allocation in a distributed camera network
Machine Vision and Applications
Multi-Camera Tracking with Adaptive Resource Allocation
International Journal of Computer Vision
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
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While multi-camera methods for object tracking tend to out-perform their single-camera counterparts, the data aggregation schemes can introduce new challenges, such as resource management and algorithm complexity. We present a framework for dynamically choosing the best subset of available cameras for tracking in real-time, which reduces aggregate tracking error and resource consumption and can be applied to a variety of existing base tracking models. We demonstrate on challenging video sequences of players in a basketball game. Our method is able to successfully track targets entering and exiting camera views and through occlusions, and overcome instances of single-view tracking drift.