Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

@article{Zhu2016BeyondLS,
  title={Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals},
  author={Gao Zhu and Fatih Murat Porikli and Hongdong Li},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={943-951}
}
Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search… CONTINUE READING
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