Crowd Density Spectrum Estimation Method Based on Local Texture Features
A technology of texture features and crowd density, applied in computing, computer parts, instruments, etc., can solve the problems of pedestrians appearing in a certain part of the image, large memory and time consumption, and high feature dimensions, to meet real-time requirements, The effect of high practicability and feasibility
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[0023] The existing technologies required by the present invention include SIFT key point detector and SVM classifier, and the adopted features are local binary pattern LBP, local ternary pattern LTP, and gray level co-occurrence matrix features.
[0024] According to the number of pedestrians contained in a unit area, we divide the crowd density into five types: very low, low, medium, high, and very high. This classification is based on the concept of the level of free movement of the crowd proposed by Polus. Polus divides crowd density into four levels: free crowd flow, restricted crowd flow, dense crowd flow, and congested crowd flow.
[0025] Main work of the present invention is divided into two phases: training phase and testing phase, as figure 1 shown.
[0026] The training phase can be divided into the following three steps:
[0027] Step 1: Sampling a large number of video samples taken in the real monitoring scene, and extracting the same amount of training pictur...
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