Traffic environment pedestrian multi-dimensional motion feature visual extraction method

Abstract

The invention discloses a traffic environment pedestrian multi-dimensional motion feature visual extraction method comprising a step 1 of constructing a pedestrian motion database; a step 2 of extracting a pedestrian detection frame image of the same pedestrian in consecutive image frames; a step 3 of extracting the HOG feature of the same pedestrian motion energy map; a step 4 of constructing a pedestrian motion pose recognition model based on an Elman neural network; a step 5 of determining a pedestrian pose in a current video by using the pedestrian motion pose recognition model based on the Elman neural network; a step 6 of calculating the instantaneous speed sequences of the pedestrian in the X-axis and Y-axis directions to obtain the real-time speed of the pedestrian; and a step 7 ofaccording to a three-dimensional scene in an intersection environment, obtaining the position information of the pedestrian in the image in real time, and obtaining the real-time motion feature of the pedestrian in combination with the pedestrian pose and the real-time speed. The method has high recognition accuracy and good robustness, is convenient to use, and has a good application and promotion space.

Description

technical field [0001] The invention belongs to the field of traffic monitoring, in particular to a method for visually extracting multi-dimensional motion features of pedestrians in a traffic environment. Background technique [0002] In recent years, with the rapid development of science and technology, more and more intelligent methods have been applied to transportation, especially in the field of intelligent driving. Traffic safety is an eternal topic. In collision accidents, collisions between vehicles and pedestrians also account for a large proportion. The timely detection and posture recognition of pedestrians is the key to the current intelligent traffic active protection system. To achieve accurate identification, the most important thing is pedestrian motion feature extraction. [0003] Pedestrian pose recognition includes global feature method and local feature method. The global feature mostly adopts the method of moving historical images, that is, the frame...

AI Technical Summary

Problems solved by technology

The patent has the following problems: 1. When extracting the motion vector of each pixel, the pixels are not effectively screened, the amount of data is large, and the calculation is complicated; 2. The clustering algorithm used in the patent is prone to local convergence

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