Abstract
Online multi-object tracking is one of the crucial tasks in time-critical computer vision applications. In this paper, the problem of online multi-object tracking in complex scenes from a single, static, un-calibrated camera is addressed. In complex scenes, it is still challenging due to frequent and prolonged occlusions, abrupt motion change of objects, unreliable detections, and so on. To handle these difficulties, this paper proposes a four-stage hierarchical association framework based on online tracking-bydetection strategy. For this framework, tracks and detections are divided into several groups depending on several cues obtained from association results with the proposed track confidence. In each association stage, different sets of tracks and detections are associated to handle the following problems simultaneously: track generation, progressive trajectory construction, track drift and fragmentation. The experimental results show the robustness and effectiveness of the proposed method compared with other state-of-the-art methods.
Original language | English |
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Title of host publication | Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 |
Publisher | IEEE Computer Society |
Pages | 1273-1281 |
Number of pages | 9 |
ISBN (Electronic) | 9781467388504 |
DOIs | |
Publication status | Published - 2016 Dec 16 |
Event | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, United States Duration: 2016 Jun 26 → 2016 Jul 1 |
Other
Other | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 |
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Country/Territory | United States |
City | Las Vegas |
Period | 16/6/26 → 16/7/1 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering