Tianxiang Bai

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Tracking multiple moving targets in a video is a challenge because of several factors, including noisy video data, varying number of targets, and mutual occlusion problems. The Gaussian mixture probability hypothesis density (GM-PHD) filter, which aims to recursively propagate the intensity associated with the multi-target posterior density, can overcome(More)
In this paper, we present a novel appearance model using sparse representation and online dictionary learning techniques for visual tracking. In our approach, the visual appearance is represented by sparse representation, and the online dictionary learning strategy is used to adapt the appearance variations during tracking. We unify the sparse(More)
We propose to explore a novel tracking system for human tracking in thermal catadioptric omnidirectional (TCO) vision, which is able to realize the surveillance in all-weather and wide field of view conditions. In contrast, previous human tracking system mainly focuses on tracking in conventional imaging system. In this paper, the proposed tracking method(More)
In this work, we propose a robust and flexible appearance model based on the structured sparse representation framework. In our method, we model the complex nonlinear appearance manifold and the occlusion as a sparse linear combination of structured union of subspaces in a basis library, which consists of multiple incremental learned target subspaces and a(More)
We propose a robust visual tracker based on structured sparse representation appearance model. The appearance of tracking target is modeled as a sparse linear combination of Eigen templates plus a sparse error due to occlusions. We address the structured sparse representation that preferably matches the practical visual tracking problem by taking the(More)
We present a novel appearance model using sparse coding with online sparse dictionary learning techniques for robust visual tracking. In the proposed appearance model, the target appearance is modeled via online sparse dictionary learning technique with an “elastic-net constraint”. This scheme allows us to capture the characteristics of the(More)
Tracking multiple moving targets in video is still a challenge because of mutual occlusion problem. This paper presents a Gaussian mixture probability hypothesis density-based visual tracking system with game theory-based mutual occlusion handling. First, a two-step occlusion reasoning algorithm is proposed to determine the occlusion region. Then, the(More)
Multi-target tracking in video is a challenge due to noisy video data, varying number of targets, and the data association problems. In this paper, a multi-target visual tracking system that incorporates object detection with the Gaussian mixture PHD filter is developed. The main contribution of this paper is to propose a new birth intensity online(More)