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Part-based trackers are effective in exploiting local details of the target object for robust tracking. In contrast to most existing part-based methods that divide all kinds of target objects into a number of fixed rectangular patches, in this paper, we propose a novel framework in which a set of deformable patches dynamically collaborate on tracking of non-rigid objects. In particular, we proposed...
We present a novel approach to non-rigid objects contour tracking in this paper based on a supervised level set model (SLSM). In contrast to most existing trackers that use bounding box to specify the tracked target, the proposed method extracts the accurate contours of the target as tracking output, which achieves better description of the non-rigid objects while reduces background pollution to the...
A novel approach based on a refined level sets method is presented in this paper for non-rigid object tracking. In contrast with conventional level sets methods, which are blind to target and emphasize the intensity consistency only, the proposed level set method is strengthened by making full use of the tracking context. By associating multiple feature spaces, the most discriminative target information...
A novel level set method based on on-line discriminative appearance modeling (DAMLSM) is presented for contour tracking. In contrast with traditional level set models which emphasize the intensity consistent segmentation and consider no priors, the proposed DAMLSM takes the context of tracking into account and use a discriminative patch based target model to guide the curve evolution. By modeling...
Occlusions are challenging issue for robust visual tracking. In this paper, motivated by the fact that a tracked object is usually embedded into context that provides useful information for estimating the target, we propose a novel tracking algorithm named Tracking with Context Prediction (TCP). The context here includes the neighboring objects and specific parts of target. The proposed method simultaneously...
We propose a novel object tracking algorithm based on modeling the target appearance in a joint space. In contrast with traditional histogram-based trackers which discard all spatial information, the joint space takes both the photometric and spatial information into account. Within this joint space, the target is modeled in a Gaussian mixtures manner where a richer description of the target is captured...
A robust object tracking algorithm is proposed in this paper based on an on-line discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration...
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