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International Journal of Computer Vision

Erschienen in:

06.09.2016

Multi-Camera Multi-Target Tracking with Space-Time-View Hyper-graph

verfasst von: Longyin Wen, Zhen Lei, Ming-Ching Chang, Honggang Qi, Siwei Lyu

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2017

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Abstract

Incorporating multiple cameras is an effective solution to improve the performance and robustness of multi-target tracking to occlusion and appearance ambiguities. In this paper, we propose a new multi-camera multi-target tracking method based on a space-time-view hyper-graph that encodes higher-order constraints (i.e., beyond pairwise relations) on 3D geometry, appearance, motion continuity, and trajectory smoothness among 2D tracklets within and across different camera views. We solve tracking in each single view and reconstruction of tracked trajectories in 3D environment simultaneously by formulating the problem as an efficient search of dense sub-hypergraphs on the space-time-view hyper-graph using a sampling based approach. Experimental results on the PETS 2009 dataset and MOTChallenge 2015 3D benchmark demonstrate that our method performs favorably against the state-of-the-art methods in both single-camera and multi-camera multi-target tracking, while achieving close to real-time running efficiency. We also provide experimental analysis of the influence of various aspects of our method to the final tracking performance.

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Many methods do not form tracklets but perform association directly on detections in each frame. In this work, we unify these methods by treating individual frame detections as tracklets of length one.

The last frame index of \({\mathcal {T}}\) and the first frame index of \({\mathcal {T}}'\) may correspond to multiple detections from different camera views.

Note that this is different from the degree of the nodes, which specifies how many hyper-edges can associate with one node.

The \(\beta \)-subhypergraph indicates the sub-hypergraph of STV hyper-graph, which includes \(\beta \) nodes.

The calculation of the number of hyper-edges, including nodes \(\nu \), \(\nu '\) and \(\nu _j\) is a combinational problem, that is to choose \(k-3\) nodes from the reliable node set \(\varOmega _i-\{\nu , \nu ', \nu _j\}\). Specifically, we set \(\rho _i = 0\) for \(|\varOmega _i| < 3\), since there does not exist enough nodes to construct a hyper-edge in that case.

We will make our method and our implementation of Hofmann et al. (2013) along with the tracking results available after the paper decision.

Since different input detections and ground truth are used, it is unfair to directly compare the tracking results of the proposed method with the results presented in Hofmann et al. (2013).

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Titel: Multi-Camera Multi-Target Tracking with Space-Time-View Hyper-graph
verfasst von: Longyin Wen
Zhen Lei
Ming-Ching Chang
Honggang Qi
Siwei Lyu
Publikationsdatum: 06.09.2016
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2017
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0943-0

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