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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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. 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. We present a novel approach to person tracking within the context of entity association. In large-scale distributed multi-camera systems, person re-identification is a challenging computer vision task as the problem is two-fold: detecting entities through identification and recognition techniques; and connecting entities temporally by associating them in often crowded environments. Since tracking essentially involves linking detections, we can reformulate it purely as a re-identification task. The inherent advantage of such a reformulation lies in the ability of the tracking algorithm to effectively handle temporal discontinuities in multi-camera environments. To accomplish this, we model human appearance, face biometric and location constraints across cameras. We do not make restrictive assumptions such as number of people in a scene. Our approach is validated by using a simple and efficient inference algorithm. Results on two publicly available datasets, CamNeT and DukeMTMC, are significantly better compared to other existing methods. In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. Experimental results reveal that with the proposed evaluation measures, the performance of T-MCT can be accurately measured, which is highly correlated to the performance of Re-id. Furthermore, it is also noted that our T-MCT framework achieves competitive score on the DukeMTMC dataset when compared to the previous work that used global optimization algorithms. Both the evaluation measures and the inter-camera tracking framework are proven to be the stepping stone for multi-camera tracking. 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. The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the statistical consistency of the observation data. The derived model is used to automatically determine the topography of a network of cameras and to provide a means for tracking targets across the "blind" areas of the network. A theoretical justification and experimental validation of the methods are provided. This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method, to model both the colour variations and posterior probability distributions of spatio-temporal links between cameras. These operate in parallel and are then used with an appearance model of the object to track across spatially separated cameras. The approach requires no pre-calibration or batch preprocessing, is completely unsupervised, and becomes more accurate over time as evidence is accumulated. This paper presents a novel and robust approach to consistent labeling for people surveillance in multicamera systems. A general framework scalable to any number of cameras with overlapped views is devised. An offline training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.