2012 | OriginalPaper | Buchkapitel
To Track or To Detect? An Ensemble Framework for Optimal Selection
verfasst von : Xu Yan, Xuqing Wu, Ioannis A. Kakadiaris, Shishir K. Shah
Erschienen in: Computer Vision – ECCV 2012
Verlag: Springer Berlin Heidelberg
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This paper presents a novel approach for multi-target tracking using an ensemble framework that optimally chooses target tracking results from that of independent trackers and a detector at each time step. The ensemble model is designed to select the best candidate scored by a function integrating detection confidence, appearance affinity, and smoothness constraints imposed using geometry and motion information. Parameters of our association score function are discriminatively trained with a max-margin framework. Optimal selection is achieved through a hierarchical data association step that progressively associates candidates to targets. By introducing a second target classifier and using the ranking score from the pre-trained classifier as the detection confidence measure, we add additional robustness against unreliable detections. The proposed algorithm robustly tracks a large number of moving objects in complex scenes with occlusions. We evaluate our approach on a variety of public datasets and show promising improvements over state-of-the-art methods.