Name
Abstract | ||
|---|---|---|
The occlusion in dynamic or clutter scene is a critical issue in multi-object tracking. Using latent variable to formulate this problem, some methods achieved state-of-the-art performance, while making an exact solution computationally intractable. In this paper, we present a hierarchical association framework to address the problem of occlusion in a complex scene taken by a single camera. At the first stage, reliable tracklets are obtained by frame-to-frame association of detection responses in a flow network. After that, we propose to formulate tracklets association problem in a spatio-temporal clustering model which presents the problem as faithfully as possible. Due to the important role that affinity model plays in our formulation, we then construct a sparsity induced affinity model under the assumption that a detection sample in a tracklet can be efficiently represented by another tracklet belonging to the same object. Furthermore, we give a near-optimal algorithm based on globally greedy strategy to deal with spatio-temporal clustering, which runs linearly with the number of tracklets. We quantitatively evaluate the performance of our method on three challenging data sets and achieve a significant improvement compared to state-of-the-art tracking systems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-37431-9_14 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
multi-object tracking,complex scene,reliable tracklets,spatio-temporal clustering model,hierarchical association framework,sparsity induced affinity model,frame-to-frame association,tracklets association problem,detection response,clutter scene,affinity model | Flow network,Computer vision,Data set,Pattern recognition,Clutter,Computer science,Tracking system,Latent variable,Greedy algorithm,Video tracking,Artificial intelligence,Cluster analysis | Conference |
Volume | Issue | ISSN |
7726 LNCS | PART 3 | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
2 | ||