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.
Multi-target Multi-camera Tracking is a challenging task due to the illumination variance, change of viewpoints and the blind area among cameras. Methods like @cite_59 @cite_48 @cite_32 @cite_31 @cite_66 @cite_39 aim to model the relationship among cameras including illumination changes, travel time and entry exit rates across pairs of cameras. Illumination always varies largely on different viewpoints, so the brightness transfer function (BTF) from a given camera to another camera is estimated to model the illumination changes. @cite_6 finds that all BTFs lie in a low dimensional subspace, and demonstrates that subspace can be used to compute appearance similarity. @cite_37 employs a Cumulative Brightness Transfer Function (CBTF) for mapping color among cameras located at different physical sites. However, the above methods only address the appearance information but ignore the spatial relationship among cameras. To solve this problem, @cite_46 uses kernel density estimation to infer the inter-camera relationships in the form of the multivariate probability density of space-time variables, then integrates spatial cue and appearance cue with the maximum likelihood estimation framework.
@cite_37 @cite_46 @cite_48 @cite_32 @cite_6 @cite_39 @cite_59 @cite_31 @cite_66
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Tracking across cameras with non-overlapping views is a challenging problem. Firstly, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, we observe that people or vehicles tend to follow the same paths in most cases, i.e., roads, walkways, corridors etc. The proposed algorithm uses this conformity in the traversed paths to establish correspondence. The algorithm learns this conformity and hence the inter-camera relationships in the form of multivariate probability density of space-time variables (entry and exit locations, velocities, and transition times) using kernel density estimation. To handle the appearance change of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace. This subspace is learned by using probabilistic principal component analysis and used for appearance matching. The proposed approach does not require explicit inter-camera calibration, rather the system learns the camera topology and subspace of inter-camera brightness transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum likelihood (ML) estimation framework using both location and appearance cues. Experiments with real world videos are reported which validate the proposed approach. The appearance of individuals captured by multiple non-overlapping cameras varies greatly due to pose and illumination changes between camera views. In this paper we address the problem of dealing with illumination changes in order to recover matching of individuals appearing at different camera sites. This task is challenging as accurately mapping colour changes between views requires an exhaustive set of corresponding chromatic brightness values to be collected, which is very difficult in real world scenarios. We propose a Cumulative Brightness Transfer Function (CBTF) for mapping colour between cameras located at different physical sites, which makes better use of the available colour information from a very sparse training set. In addition we develop a bi-directional mapping approach to obtain a more accurate similarity measure between a pair of candidate objects. We evaluate the proposed method using challenging datasets obtained from real world distributed CCTV camera networks. The results demonstrate that our bi-directional CBTF method significantly outperforms existing techniques. We present a distributed system for wide-area multi-object tracking across disjoint camera views. Every camera in the system performs multi-object tracking, and keeps its own trackers and trajectories. The data from multiple features are exchanged between adjacent cameras for object matching. We employ a probabilistic Petri Net-based approach to account for the uncertainties of the vision algorithms (such as unreliable background subtraction, and tracking failure) and to incorporate the available domain knowledge. We combine appearance features of objects as well as the travel-time evidence for target matching and consistent labeling across disjoint camera views. 3D color histogram, histogram of oriented gradients, local binary patterns, object size and aspect ratio are used as the appearance features. The distribution of the travel time is modeled by a Gaussian mixture model. Multiple features are combined by the weights, which are assigned based on the reliability of the features. By incorporating the domain knowledge about the camera configurations and the information about the received packets from other cameras, certain transitions are fired in the probabilistic Petri net. The system is trained to learn different parameters of the matching process, and updated online. We first present wide-area tracking of vehicles, where we used three non-overlapping cameras. The first and the third cameras are approximately 150 m apart from each other with two intersections in the blind region. We also present an example of applying our method to a people-tracking scenario. The results show the success of the proposed method. A comparison between our work and related work is also presented. When viewed from a system of multiple cameras with non-overlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in another camera view due to the differences in illumination, pose and camera parameters. In order to handle the change in observed colors of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace and demonstrate that this subspace can be used to compute appearance similarity. In the proposed approach, the system learns the subspace of inter-camera brightness transfer functions in a training phase during which object correspondences are assumed to be known. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both location and appearance cues. We evaluate the proposed method under several real world scenarios obtaining encouraging results.