Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras. Recently, the tracking performance of MTMCT is significantly enhanced with the employment of re-identification (Re-ID) model. However, the appearance feature usually becomes unreliable due to the occlusion and orientation variance of the targets. Directly applying Re-ID model in MTMCT will encounter the problem of identity switches (IDS) and tracklet fragment caused by occlusion. To solve these problems, we propose a novel tracking framework in this paper. In this framework, the occlusion status and orientation information are utilized in Re-ID model with human pose information considered. In addition, the tracklet association using the proposed fused tracking feature is adopted to handle the fragment problem. The proposed tracker achieves 81.3 IDF1 on the multiple-camera hard sequence, which outperforms all other reference methods by a large margin.
In addition, numerous graph-based models @cite_15 @cite_68 @cite_54 @cite_51 @cite_61 @cite_45 @cite_38 are proposed to deal with MTMCT. @cite_15 constructs a mini-cost flow graph to complete data association among cameras in 3D world space. In @cite_68 , the data association is formulated as a constrained flow optimization of a convex problem, and the problem is solved by the k-shortest paths algorithm. In @cite_45 , Yoon exploit the multiple hypothesis tracking (MHT) algorithm and apply it on MTMCT with some modifications. Branches in track-hypothesis trees represent the trajectory across multiple cameras. Maximum Weight Independent Set (MWIS) in @cite_55 is adopted for computing the best hypothesis set. With the development of Re-ID, a number of methods @cite_45 @cite_2 @cite_2 @cite_42 @cite_30 adopt Re-ID technology to represent the appearance of the target. In @cite_30 , Ristani learn a good feature for both MTMCT and Re-ID with a convolutional neural network. In @cite_42 , Zhang obtain a good result with simple hierarchical clustering and well-trained Re-ID feature.
@cite_61 @cite_38 @cite_30 @cite_54 @cite_55 @cite_42 @cite_45 @cite_2 @cite_15 @cite_68 @cite_51
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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, a gating technique which eliminates the unlikely observation-to-track association using space-time information has been introduced. In the experiments, the proposed method has been tested using two datasets, DukeMTMC and NLPR , which demonstrates that the method outperforms the state-of-the-art method in terms of improvement of the accuracy. In addition, real-time and online performance of proposed method is also showed in this study. Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work, we propose a novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multicut problem. Our model generalizes previous works by introducing a mechanism for adding long-range attractive connections between nodes in the graph without modifying the original set of feasible solutions. This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions. To effectively match hypotheses over longer temporal gaps we develop new deep architectures for re-identification of people. They combine holistic representations extracted with deep networks and body pose layout obtained with a state-of-the-art pose estimation model. We demonstrate the effectiveness of our formulation by reporting a new state-of-the-art for the MOT16 benchmark. The code and pre-trained models are publicly available. This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multitarget tracking in a cluttered environment. When the number of targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association probabilities in JPDA is NP-hard, we prove that the single-scan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for JPDA. For general multitarget tracking problems, in which unknown numbers of targets appear and disappear at random times, we present a multi-scan MCMCDA algorithm that approximates the optimal Bayesian filter. We also present extensive simulation studies supporting theoretical results in this paper. Our simulation results also show that MCMCDA outperforms multiple hypothesis tracking (MHT) by a significant margin in terms of accuracy and efficiency under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates. This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best matching tracklets that do not violate hard and soft contextual constraints between the resulting tracks. We prove that this data association problem can be formulated as finding the maximum-weight independent set (MWIS) of the graph. We present a new, polynomial-time MWIS algorithm, and prove that it converges to an optimum. Similarity and contextual constraints between object detections, used for data association, are learned online from object appearance and motion properties. Long-term occlusions are addressed by iteratively repeating MWIS to hierarchically merge smaller tracks into longer ones. Our results demonstrate advantages of simultaneously accounting for soft and hard contextual constraints in multitarget tracking. We outperform the state of the art on the benchmark datasets.