Re-identification of pedestrians in crowds using dynamic time warping
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Spatio-Temporal clustering model for multi-object tracking through occlusions
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Multi-target tracking on confidence maps: An application to people tracking
Computer Vision and Image Understanding
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
Online parameter tuning for object tracking algorithms
Image and Vision Computing
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We address the problem of multi-person tracking in a complex scene from a single camera. Although tracklet-association methods have shown impressive results in several challenging datasets, discriminability of the appearance model remains a limitation. Inspired by the work of person identity recognition, we obtain discriminative appearance-based affinity models by a novel framework to incorporate the merits of person identity recognition, which help multi-person tracking performance. During off-line learning, a small set of local image descriptors is selected to be used in on-line learned appearances-based affinity models effectively and efficiently. Given short but reliable track-lets generated by frame-to-frame association of detection responses, we identify them as query tracklets and gallery tracklets. For each gallery tracklet, a target-specific appearance model is learned from the on-line training samples collected by spatio-temporal constraints. Both gallery tracklets and query tracklets are fed into hierarchical association framework to obtain final tracking results. We evaluate our proposed system on several public datasets and show significant improvements in terms of tracking evaluation metrics.