Population density detection of video images, a variety of crowd density scenes crowd counting algorithm: Algorithm functions: a mathematical relation between the image features and the number of images Algorithm Input: training sample image 1,2 ... K Algorithm output: model estimation parameters, reference image Algorithmic process: 1) the training sample image into blocks (algorithm 1.1) 2) by 1.2 algorithm to calculate the corresponding training samples of each block ALBP features normalized, then K-means algorithm (algorithm can be used opencv library implementation, no longer describe the algorithm), the image block is divided into k (k <K) class, gets k (k <K) clustering centers, namely the reference image 3) conduct of image block matching with the reference image. 1.2 ALBP characterized using an algorithm to strike, and strike the similarity, the similarity of a set of new features and forming a normalized new features. 4) pedestrian area accounted for in

PeopleDensitydll\Debug\PeopleDensitydll.dll
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PeopleDensitydll

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