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In this paper, we propose Local Adaptive Hybrid Pattern (LAHP) to use intrinsic properties, both edge and color, of each pixel adaptively (inspired from LHP based methods) while using a single feature representation (inspired from LOBSTER). The proposed LAHP encodes edge and color information, and an adaptive factor based on gradient magnitude together as a single feature. We introduce a way to calculate...
In this paper, we propose a novel edge descriptor method for background modeling. In comparison to previous edge-based local-pattern methods, it is more robust to noise and illumination variations due to the use of principal gradient information in a local neighborhood. For the background modeling problem, we combined the proposed method with the Local Hybrid Pattern and experimented with an adaptive-dictionary-model...
For background-subtraction-based moving object detection, reliable background modeling is the most important component. Pixel-based methods are sensitive to illumination change, and edge-based methods can solve illumination-related problems, but have shape distortion problems. In this paper, we propose an edge-segment-based statistical background modeling algorithm and an online update mechanism to...
Background modeling is challenging due to background dynamism. Most background modeling methods fail in the presence of intensity changes, because the model cannot handle sudden changes. A solution to this problem is to use intensity-robust features. Despite the changes of an edge's shape and position among frames, edges are less sensitive than a pixel's intensity to illumination changes. Furthermore,...
The detection of moving objects depends on the accuracy of the model used to represent the background. Common pixel-based and naive edge-based approaches have many drawbacks in dynamic environments, e.g., false detections with noise. We propose a novel background model that encodes the background as edges, building a statistical distribution per segment that represents the edge behavior. We build...
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