In this paper, a unified three-layer hierarchical approach for solving tracking problem in a multiple non-overlapping cameras setting is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and then, in the third layer, we solve across-camera tracking by associating tracks of the same person in all cameras simultaneously. To best serve our purpose, we propose fast-constrained dominant set clustering (FCDSC), a novel method which is several orders of magnitude faster (close to real time) than existing methods. FCDSC is a parameterized family of quadratic programs that generalizes the standard quadratic optimization problem. In our method, we first build a graph where nodes of the graph represent short-tracklets, tracklets and tracks in the first, second and third layer of the framework, respectively. The edge weights reflect the similarity between nodes. FCDSC takes as input a constrained set, a subset of nodes from the graph which need to be included in the extracted cluster. Given a constrained set, FCDSC generates compact clusters by selecting nodes from the graph which are highly similar to each other and with elements in the constrained set. We have tested this approach on a very large and challenging dataset (namely, MOTchallenge DukeMTMC) and show that the proposed framework outperforms the state-of-the-art approaches. Even though the main focus of this paper is on multi-target tracking in non-overlapping cameras, the proposed approach can also be applied to solve video-based person re-identification problem. We show that when the re-identification problem is formulated as a clustering problem, FCDSC can be used in conjunction with state-of-the-art video-based re-identification algorithms, to increase their already good performances. Our experiments demonstrate the general applicability of the proposed framework for multi-target multi-camera tracking and person re-identification tasks.

Multi-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets

Marcello Pelillo;
2019

Abstract

In this paper, a unified three-layer hierarchical approach for solving tracking problem in a multiple non-overlapping cameras setting is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and then, in the third layer, we solve across-camera tracking by associating tracks of the same person in all cameras simultaneously. To best serve our purpose, we propose fast-constrained dominant set clustering (FCDSC), a novel method which is several orders of magnitude faster (close to real time) than existing methods. FCDSC is a parameterized family of quadratic programs that generalizes the standard quadratic optimization problem. In our method, we first build a graph where nodes of the graph represent short-tracklets, tracklets and tracks in the first, second and third layer of the framework, respectively. The edge weights reflect the similarity between nodes. FCDSC takes as input a constrained set, a subset of nodes from the graph which need to be included in the extracted cluster. Given a constrained set, FCDSC generates compact clusters by selecting nodes from the graph which are highly similar to each other and with elements in the constrained set. We have tested this approach on a very large and challenging dataset (namely, MOTchallenge DukeMTMC) and show that the proposed framework outperforms the state-of-the-art approaches. Even though the main focus of this paper is on multi-target tracking in non-overlapping cameras, the proposed approach can also be applied to solve video-based person re-identification problem. We show that when the re-identification problem is formulated as a clustering problem, FCDSC can be used in conjunction with state-of-the-art video-based re-identification algorithms, to increase their already good performances. Our experiments demonstrate the general applicability of the proposed framework for multi-target multi-camera tracking and person re-identification tasks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3717301
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