A Method for Estimating Vehicle Pedestrian Areas Based on Optical Flow Clustering

Abstract

The invention discloses a vehicle-mounted pedestrian area estimation method based on optical flow clustering, which performs optical flow estimation on images acquired by a camera to obtain an image optical flow field; then performs optical flow clustering to estimate the background area; and eliminates the background area , use the graph segmentation algorithm to segment the foreground area; finally, distinguish each area in the foreground area, and identify the effective pedestrian area. The method for estimating the vehicle pedestrian area based on optical flow clustering proposed by the present invention avoids the use of global templates in traditional pedestrian detection systems. The blind search for pedestrian recognition caused by search matching recognition is suitable for vehicle intelligent assisted driving and unmanned driving in the vehicle environment. It overcomes the problem that the usual EM algorithm and clustering algorithms such as K-means are difficult to determine the Gaussian distribution of the background. The optical flow clustering algorithm proposed in this method can estimate the background area more effectively. In the process of identifying the foreground pedestrian area, the human body shape feature can be used to effectively eliminate the non-pedestrian area and obtain the pedestrian area.

Description

technical field [0001] The invention belongs to the field of intelligent transportation and intelligent vehicle research, and relates to a vehicle-mounted pedestrian area estimation method based on optical flow clustering, which is suitable for quickly locating pedestrian areas in a pedestrian detection system. Background technique [0002] In recent years, research results such as Haar wavelet pedestrian detection method, human body shape detection method, HOG+SVM pedestrian detection method have been gradually formed, which have promoted the development of intelligent driving technology. These studies have adopted the pedestrian template matching method of sliding window to identify pedestrians. However, due to the global multi-scale window search strategy of template matching, a large number of blind searches and time waste are caused in pedestrian detection, which reduces the real-time performance of pedestrian detection. The method of obtaining the possible pedestrian a...

AI Technical Summary

Problems solved by technology

Although this type of method can obtain pedestrian ROI, the complex model calculation increases the running time of pedestrian detection. (2) Motion detection method, which uses optical flow calculation and motion segmentation method to obtain pedestrian area

For example, Elzein et al. provided a new vision for pedestrian ROI estimation from the perspective of motion segmentation, but this method lacks consideration of the motion difference characteristics of motion scenes and pedestrians, and still cannot reduce the computational complexity of optical flow field segmentation.

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