MATLAB Source Code for Pedestrian Segmentation from Background
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Resource Overview
This program provides MATLAB source code for separating pedestrians from background using pre-extracted HOG features. The implementation includes both blocked and non-blocked feature extraction approaches, where blocking involves dividing images into smaller segments for more detailed feature representation. Classification is performed using minimum Euclidean distance method for enhanced pedestrian detection accuracy.
Detailed Documentation
This program provides MATLAB source code for separating pedestrians from background. The implementation utilizes pre-extracted HOG (Histogram of Oriented Gradients) features for both background and pedestrian regions, which are categorized into two groups: non-blocked and blocked features. The blocking approach involves dividing images into smaller segments to extract more granular features, enhancing feature discrimination capability. The classification process employs the minimum Euclidean distance method to distinguish pedestrians from background elements, significantly improving algorithm accuracy through optimized feature matching and distance-based decision boundaries. The code structure includes feature extraction modules, distance calculation functions, and classification logic that can be easily integrated into larger computer vision systems.
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