In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. The authors' method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Furthermore, multi-target tracking within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis. Each status represents three different stages of a multi-camera track: tracking, searching, and end-of-track. The tracking status means targets are tracked by a single camera tracker. In the searching status, the disappeared targets are examined if they reappear in other cameras. The end-of-track status does the target exited the camera network due to its lengthy invisibility. These three status assists MHT to form the track-hypothesis trees for multi-camera tracking. Furthermore, they present a gating technique for eliminating of unlikely observation-to-track association. In the experiments, they evaluate the proposed method using two datasets, DukeMTMC and NLPR_MCT, which demonstrates that the proposed method outperforms the state-of-the-art method in terms of improvement of the accuracy. In addition, they show that the proposed method can operate in real-time and online. A large number of cameras recently have been deployed to cover wide area. Besides, tracking multiple targets in a camera network becomes an important and challenging problem in visual surveillance systems since in-person monitoring wide area is costly and needs a lot of effort. Hence, it is desirable to develop multi-target multi-camera tracking (MTMCT) algorithm. In this paper, our goal is to develop an algorithm that can track multiple targets (especially for pedestrians in this work) in a camera network. The targets may move within a camera or move to another camera and the coverage of each camera does not overlap. To achieve this goal, we need to solve both single camera tracking (SCT) and multi-camera tracking (MCT). There has been great amount of effort made to SCT whereas relatively smaller amount of effort has been done for MCT with disjoint views. Moreover, most MCT approaches [1] [2] [3] only focus on tracking targets across cameras by assuming solved SCT in advance; thus, jointly tracking multiple targets in both within and across cameras still remains to be explored much further [4] . The proposed MHT algorithm tracks targets across cameras by maintaining the identities of observations which are obtained by solving SCT that tracks targets in within-camera. Thus, our method jointly tracks targets in both within and across cameras. In this work, we adopt the real-time and online method[5] to produce observations by tracking multiple targets in within-camera. These observations obtained from each camera are fed into the proposed MHT algorithm which solves MCT problem. The proposed MHT algorithm forms track-hypothesis trees with obtained observations either by adding a child node to hypothesis tree, which describes the association between an observation and an existing track hypothesis, or by creating a new tree with one root node indicating an observation, which describes the initiation of a new multi-camera track. Each branch in track-hypothesis trees represents different across camera data association result (i.e., a multi-camera track). To work in concert with SCT, every node in track-hypothesis trees designates certain observation and all leaf nodes have a status. There are three statuses for the proposed MHT and each of which represents a different stage of a multi-camera track, tracking, searching, and end-of-track. With the status, the MHT can form the track-hypothesis trees while simultaneously solving SCT to produce observations. Then it selects the best set of track hypotheses as the multi-camera tracks from the track-hypothesis trees. Furthermore, we propose gating mechanism to eliminate unlikely observation-to-track pairing; this also prevents track-hypothesis trees from unnecessary growth. We propose two gating mechanisms, speed gating and temporal gating in order to deal different tracking scenarios (tracking targets on the ground plane or image plane). For the appearance feature of an observation, we used simple averaged color histogram as an appearance model after Convolutional Pose Machine[6] is applied to an image patch of a person in order to capture the pose variation. The experimental results shows that our method achieves state-of-the-art performance on DukeMTMC dataset and performs comparable to the state-of-theart method on NLPR_MCT dataset. Furthermore, we demonstrate that proposed method is able to operate in real-time with real-time SCT in Section 4.5. The remainder of this paper organized as follows. In Section 2, we review relevant previous works. The detailed explanation of proposed method is given in Section 3. Section 3.1 describes how the proposed MHT forms track-hypothesis trees while it simultaneously works with SCT. The proposed gating mechanism is explained in Section 3.2. In Section 4, we report experiment results with conducted on DukeMTMC and NLPR_MCT datasets. Finally, we conclude the paper in Section 5. Single camera tracking (SCT), which tracks multiple targets in a single scene, is also called multi-object tracking (MOT). Many approaches have been proposed to improve the MOT. Track-bydetection, which optimizes a global objective function over many frames have emerged as a powerful MOT algorithm in recent years [7] . Network flow-based methods are successful approaches in track-by-detection techniques [8] [9] [10] . These methods efficiently optimize their objective function using the push-relabel method [9] pp. 1-10 1
Authors in REF used track-hypothesis trees to solve tracking in multiple cameras.
Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views
IET image processing, Volume: 12, Issue: 7, 7 2018
Computer Science Mathematics