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Moving object detection and tracking in video sequ
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In the series of video surveillance, the paper of moving object detection and human tracking, the normalization problem in libsvm is divided into two parts, one is the normalization of training samples, the other is the normalization of test samples, According to the SVM in windows provided by 2.89- scale.exe The code of the program is in the root directory of 2.89vc version. The SVM- scale.exe When normalizing the training sample, the parameter line used is like this
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Hog feature extraction
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HOG's core idea is the local objects can be detected by light intensity gradients or edge directions of distribution are described. By connecting the whole image into smaller regions (called cells), each cell generates a histogram of oriented gradients or pixel cell edge direction histogram of these combinations can be expressed (the target goal) descriptor. To improve accuracy, the local histograms can be evaluated in a larger area of the image (known as block) comparison of light intensity as the measure was standardized, and then using this value (measure) the normalized all the cells in the block. the normalization process is better done by sunlight/shadow invariance. Compared with other descriptors, HOG descriptors to maintain the geometrical and optical transformation invariance (unless the object direction changing). So HOG descriptor is especially suited for human detection. Popular speaking: HOG feature extraction method is to add an image: 1. gray-scale (u
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