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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...
Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) challenge many traditional background subtraction methods. In this paper, we present a novel background subtraction approach for dynamic scenes, in which the background is modeled in a multi-resolution framework. First, for each level of the pyramid, we run an independent mixture of Gaussians models (GMM)...
We present a novel feature extraction framework, Neighboring Image Patches Embedding (NIPE), for robust and efficient background modeling. We divide image into patches and represent each image patch as a NIPE vector. Then, the background model of each image patch is constructed as a group of weighted adaptive NIPE vectors. The NIPE feature vector, whose components are similarities between current...
Traditional background subtraction methods model only temporal variation of each pixel. However, there is also spatial variation in real word due to dynamic background such as waving trees, spouting fountain and camera jitters, which causes the significant performance degradation of traditional methods. In this paper, a novel spatial-temporal nonparametric background subtraction approach (STNBS) is...
Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of ldquovisual...
We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary...
Background subtraction in dynamic scenes is an important and challenging task. In this paper, we present a novel and effective method for dynamic background subtraction based on covariance matrix descriptor. The algorithm integrates two distinct levels: pixel level and region level. At the pixel level, spatial properties that are obtained from pixel coordinate values, and appearance properties, i...
Traditional background modeling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poorly in dynamic scenes. In this paper, we present a solution to this problem. We first extend the local binary patterns from spatial domain to spatio-temporal domain, and present a new online dynamic texture extraction operator,...
This work aims at developing a scalable vision-based location recognition system where the backend database can be updated incrementally. Our proposed framework enables incremental indexing of vocabulary tree model, which efficiently includes new data into model refinement without re-generating entire model from overall dataset. An adaption trigger criterion is presented to lessen system computational...
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