This paper presents a novel introduction of online target-specific metric learning in track fragment (tracklet) association by network flow optimization for long-term multi-person tracking. Different from other network flow formulation, each node in our network represents a tracklet, and each edge represents the likelihood of neighboring tracklets belonging to the same trajectory as measured by our proposed affinity score. In our method, target-specific similarity metrics are learned, which give rise to the appearance-based models used in the tracklet affinity estimation. Trajectory-based tracklets are refined by using the learned metrics to account for appearance consistency and to identify reliable tracklets. The metrics are then re-learned using reliable tracklets for computing tracklet affinity scores. Long-term trajectories are then obtained through network flow optimization. Occlusions and missed detections are handled by a trajectory completion step. Our method is effective for long-term tracking even when the targets are spatially close or completely occluded by others. We validate our proposed framework on several public datasets and show that it outperforms several state of art methods.
[1]
Gang Wang,et al.
Pedestrian detection in highly crowded scenes using “online” dictionary learning for occlusion handling
,
2014,
2014 IEEE International Conference on Image Processing (ICIP).
[2]
Charless C. Fowlkes,et al.
Globally-optimal greedy algorithms for tracking a variable number of objects
,
2011,
CVPR 2011.
[3]
Gang Wang,et al.
Tracklet Association with Online Target-Specific Metric Learning
,
2014,
2014 IEEE Conference on Computer Vision and Pattern Recognition.
[4]
Gang Wang,et al.
Human Detection with Occlusion Handling by Over-Segmentation and Clustering on Foreground Regions
,
2012,
ACCV Workshops.
[5]
H. Damasio,et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision
,
1998
.
[6]
David A. McAllester,et al.
Object Detection with Discriminatively Trained Part Based Models
,
2010,
IEEE Transactions on Pattern Analysis and Machine Intelligence.