Gao, Shan ; Ye, Qixiang ; Xing, Junliang ; Kuijper, Arjan ; Han, Zhenjun ; Jiao, Jianbin ; Ji, Xiangyang (2017)
Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation.
In: IEEE Transactions on Image Processing, 26 (12)
doi: 10.1109/TIP.2017.2708901
Article, Bibliographie
Abstract
Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing group-based methods have extensively investigated how to make group division more accurately in a tracking-by-detection framework; however, few of them quantify the group dynamics from the perspective of targets' spatial topology or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, we propose a novel socio-topology model with a topology-energy function to factor the group dynamics of moving persons and groups. In this model, minimizing the topologyenergy- variance in a two-level energy form is expected to produce smooth topology transitions, stable group tracking, and accurate target association. To search for the strong minimum in energy variation, we design the discrete group-tracklet jump moves embedded in the gradient descent method, which ensures that the moves reduce the energy variation of group and trajectory alternately in the varying topology dimension. Experimental results on both RGB and RGB-D data sets show the superiority of our proposed model for multiple person tracking in crowd scenes.
| Item Type: | Article |
|---|---|
| Erschienen: | 2017 |
| Creators: | Gao, Shan ; Ye, Qixiang ; Xing, Junliang ; Kuijper, Arjan ; Han, Zhenjun ; Jiao, Jianbin ; Ji, Xiangyang |
| Type of entry: | Bibliographie |
| Title: | Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation |
| Language: | English |
| Date: | 2017 |
| Journal or Publication Title: | IEEE Transactions on Image Processing |
| Volume of the journal: | 26 |
| Issue Number: | 12 |
| DOI: | 10.1109/TIP.2017.2708901 |
| URL / URN: | https://doi.org/10.1109/TIP.2017.2708901 |
| Abstract: | Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing group-based methods have extensively investigated how to make group division more accurately in a tracking-by-detection framework; however, few of them quantify the group dynamics from the perspective of targets' spatial topology or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, we propose a novel socio-topology model with a topology-energy function to factor the group dynamics of moving persons and groups. In this model, minimizing the topologyenergy- variance in a two-level energy form is expected to produce smooth topology transitions, stable group tracking, and accurate target association. To search for the strong minimum in energy variation, we design the discrete group-tracklet jump moves embedded in the gradient descent method, which ensures that the moves reduce the energy variation of group and trajectory alternately in the varying topology dimension. Experimental results on both RGB and RGB-D data sets show the superiority of our proposed model for multiple person tracking in crowd scenes. |
| Uncontrolled Keywords: | People tracking, Topology, RGB color space |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Mathematical and Applied Visual Computing |
| Date Deposited: | 04 May 2020 08:45 |
| Last Modified: | 25 Nov 2021 13:55 |
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