Pedestrian local feature big data hybrid extraction method

Abstract

The invention discloses a pedestrian local feature big data hybrid 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 continuous 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 a support vector machine; a step 5 of determining a pedestrian pose in a current video by using the pedestrian motion pose recognition model based on the support vector machine; 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 of according 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 combinationwith the pedestrian pose and the real-time speed. The method can obtain more comprehensive and useful information, and has a wide signal detection range, complete target information, and high cost performance and is suitable for promotion.

Description

technical field [0001] The invention belongs to the field of traffic monitoring, and in particular relates to a large data mixed extraction method of pedestrian local characteristics. Background technique [0002] Nowadays, big data is applied in more and more extensive fields, and has had a profound impact on human society. With the opening of the national policy of "Made in China 2025", big data has already had an impact on transportation, industry, medical care, energy, climate and other fields. obvious impact. In the field of transportation, ensuring the safety of human property is always the first priority. People have been making continuous efforts to explore and improve. However, pedestrian traffic accidents are still common. How to effectively protect the safety of pedestrians’ lives and property is a key issue in the field of transportation. The point is also difficult. [0003] The difficulty of pedestrian detection lies in how to effectively identify pedestrian ...

AI Technical Summary

Problems solved by technology

[0003] The difficulty of pedestrian detection lies in how to effectively identify pedestrian movements. Human motion has non-rigid body characteristics, and there are obvious differences in shape, speed, appearance, and clothing between different individuals, irregular motion, and different motion directions. Reasons for Difficulty in Pedestrian Detection

Traditional pedestrian detection methods mainly include: microwave method, beam method, and pressure sensor method. These methods generally have limitations in detection locations and low accuracy, and cannot use specific and accurate pedestrian information provided by images.

Images