Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments

Multi-camera Tracklet association and fusion using ensemble of visual andgeometric cues

International audienceData association and fusion is pivot for object trackingin multi-camera network. We present a novel frameworkfor solving online multi-object tracking in partially overlappingmulti-camera network by modelling tracklet associationas combinatorial optimization problem hypothesizedon ensemble of cues such as appearance, motion and geometryinformation. Our method learns discriminant weightas a measure of consistency and discriminancy of featurepatterns to make ensemble feature selection and combinationbetween local and global tracking information. Ourapproach contributes uniquely in the way tracklet selection,association and fusion is done. Once multi-view correspondencesare established using planar homography, DynamicTime Warping algorithm is used to make tracklet selectionfor which similarity has to be calculated i.e overlappingtracklets and subtracklets. Then trajectory similarities arecomputed for these selective tracklets and subtracklets usingensemble of appearance and motion cues weighted byonline learnt discriminative function. Later on, we tacklethe association problem by building a k-partite graph andassociation rules to match all the pair-wise trackets. Finally,from outcome of hungarian algorithm, the associatedtrajectories are later fused. Fusion is done based on calculatedindividual tracklet reliability criteria. Experimentalresults demonstrate our system achieve performance thatsignificantly improve the state of the art on PETS 2009

Detecting and Tracking Cells using Network Flow Programming

We propose a novel approach to automatically detecting and tracking cell populations in time-lapse images. Unlike earlier ones that rely on linking a predetermined and potentially under-complete set of detections, we generate an overcomplete set of competing detection hypotheses. We then perform detection and tracking simultaneously by solving an integer program to find an optimal and consistent subset. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging image sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques

Unveiling the Power of Self-supervision for Multi-view Multi-human Association and Tracking

Multi-view multi-human association and tracking (MvMHAT), is a new but important problem for multi-person scene video surveillance, aiming to track a group of people over time in each view, as well as to identify the same person across different views at the same time, which is different from previous MOT and multi-camera MOT tasks only considering the over-time human tracking. This way, the videos for MvMHAT require more complex annotations while containing more information for self learning. In this work, we tackle this problem with a self-supervised learning aware end-to-end network. Specifically, we propose to take advantage of the spatial-temporal self-consistency rationale by considering three properties of reflexivity, symmetry and transitivity. Besides the reflexivity property that naturally holds, we design the self-supervised learning losses based on the properties of symmetry and transitivity, for both appearance feature learning and assignment matrix optimization, to associate the multiple humans over time and across views. Furthermore, to promote the research on MvMHAT, we build two new large-scale benchmarks for the network training and testing of different algorithms. Extensive experiments on the proposed benchmarks verify the effectiveness of our method. We have released the benchmark and code to the public