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This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different...
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...
In this paper, a novel standard variance feature is proposed for background modeling in dynamic scenes involving waving trees and ripples in water. The standard variance feature is the standard variance of a set of pixels' feature values, which captures mainly co-occurrence statistics of neighboring pixels in an image patch. The background modeling method based on standard variance feature includes...
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...
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...
This paper proposes a novel texture pattern flow (TPF) for complex background modeling and motion detection. The pattern flow is proposed to encode the binary pattern changes among the neighborhoods in the space-time domain. To model the distribution of the TPF, the TPF integral histograms are used to extract the discriminative features to represent the input video. Experimental results on the public...
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