In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our 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.
Multiple Hypothesis Tracking(MHT) was first presented in @cite_23 and is regarded as one of the earliest successful algorithm for visual tracking. MHT maintains all track hypotheses by building track-hypothesis trees whose branch represent a possible data association result(a track hypothesis). The probability of a track hypothesis is computed by evaluating the quality of data association result the branch had. An ambiguity of data association which occurs due to either short occlusion or missed detection does not usually matter for MHT since the best hypothesis is computed with higher-order data association information and entire track hypotheses. In this paper, we applied MHT to solve the multi-camera tracking problem.
@cite_23
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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. An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the k-best hypotheses are determined in polynomial time using an algorithm due to Murly (1968). The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination, and continuation are demonstrated together with the latter's capability to provide low level support of temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 51 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed, and experimental results show that the algorithm is robust to errors in the motion model. An investigation of the performance of the algorithm as a function of look-ahead (tree depth) indicates that high accuracy can be obtained for tree depths as shallow as three. Experimental results suggest that a real-time MHT solution to the motion correspondence problem is possible for certain classes of scenes. This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge. In this paper, we present a new multiple hypothesestracking (MHT) approach. Our tracking method is suitablefor online applications, because it labels objects at everyframe and estimates the best computed trajectories up tothe current frame. In this work we address the problems ofobject merging and splitting (occlusions) and object fragmentations.Object fragmentation resulting from imperfectbackground subtraction can easily be confused with splittingobjects in a scene, especially in close range surveillanceapplications. This subject is not addressed in mostMHT methods. In this work, we propose a framework forMHT which distinguishes fragmentation and splitting usingtheir spatial and temporal characteristics and by generatinghypotheses only for splitting cases using observation inlater frames. This approach results in a more accurate dataassociation and a reduced size of the hypothesis graph. Ourtracking method is evaluated with various indoor videos.