Unlock instant, AI-driven research and patent intelligence for your innovation.

Road Pedestrian Classification Method

A pedestrian and road technology, applied in the field of moving target tracking processing, can solve the problem of pedestrians turning into dangerous pedestrians

Active Publication Date: 2020-10-09
DALIAN NATIONALITIES UNIVERSITY
View PDF16 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are many unexpected situations on the road that may cause pedestrians to change from ordinary pedestrians to dangerous pedestrians in an instant

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Road Pedestrian Classification Method
  • Road Pedestrian Classification Method
  • Road Pedestrian Classification Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0115] Classification of repulsive pedestrians

[0116] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows figure 2 shown. figure 2 Three frames of images in consecutive video frames and the pedestrian classification results of this frame are listed, and only the repulsive force probability of the pedestrian's magnetic force meets the determination requirements of the magnetic force pedestrian. In the video, three pedestrian targets are moving at a speed of about 1.2m / s, two of them are moving in the positive direction, and one is moving in the negative direction, and they all keep moving in a straight line without changing the moving speed. From frame 8 to frame 33, pedestrians B and C keep approaching. Until the 33rd frame, the repulsion probability of pedestrians B and C exceeds δ, and they are judged as repulsive pedestrians. Similarly, at the 72nd frame, the repulsion probability of pedestrians A and C exce...

Embodiment 2

[0118] Suction Pedestrian Classification Case

[0119] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows image 3 shown. image 3 Three frames of images in consecutive video frames and the pedestrian classification results of this frame are listed, among which the magnetic probability of pedestrians and only the attractive probability meet the determination requirements of magnetic pedestrians. In the video, the three pedestrian targets move in the forward direction at a speed of about 1.2m / s, and they all keep moving in a straight line without changing the moving speed. From frame 11 to frame 39, pedestrians B and C keep walking together. Until the 39th frame, the suction probability of pedestrians B and C exceeds δ, and they are judged as suction pedestrians. In the subsequent 75th frame, pedestrians B and C maintain the determination result of the suction pedestrian.

Embodiment 3

[0121] Classification of non-magnetic pedestrians

[0122] This example is aimed at the classification of non-magnetic pedestrians. The simulation results are as follows Figure 4 shown. Figure 4 Three frames of images in consecutive video frames and the pedestrian classification results of this frame are listed, among which the magnetic probability of pedestrians and only the non-magnetic probability meet the determination requirements of magnetic pedestrians. In the video, the three pedestrian targets move in the negative direction at different speeds, and they all keep moving in a straight line without changing the moving speed. The speed of pedestrian A is about 0.5m / s, and the speed of pedestrians B and C is about 1.3m / s . At frame 9, pedestrian A's non-magnetic probability is calculated as 1, and he is judged as a non-magnetic pedestrian. In the subsequent 42nd and 103th frames, pedestrian A maintains the determination result of non-magnetic pedestrian.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The road pedestrian classification method belongs to the field of moving target tracking processing. In order to solve the problem of enriching the current road pedestrian classification, the road pedestrian images are captured by the vehicle-mounted camera, and the road pedestrians are classified by the magnetic relationship between the road pedestrians displayed in the image. The above magnetic relationship is characterized by the moving speed of pedestrians, the relative distance between pedestrians in the image and the relative distance between pedestrians and the camera, and the moving speed of pedestrians and the relative distance between pedestrians in the image And the relative distance between the pedestrian and the camera is classified, and the determination of the magnetic relationship can be used as an important reference for autonomous vehicles or assisted driving systems in obstacle avoidance and path planning.

Description

technical field [0001] The invention belongs to the field of moving target tracking processing, and specifically relates to a classification method for distinguishing potential danger levels of road pedestrians by using a magnetic force model. Background technique [0002] Moving target tracking processing technology is an important research topic in the field of machine vision, and with the application of autonomous vehicles and assisted driving systems, how to reasonably use target tracking processing technology to protect the safety of pedestrians and vehicles is also a hot research direction now . [0003] At present, when only on-board cameras are used, classifying pedestrians by analyzing information such as their historical trajectory and moving speed is a major way to use target tracking processing technology to protect the safety of pedestrians and vehicles. Firstly, the pedestrian's trajectory and speed are analyzed to calculate the probability that the pedestrian...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/103
Inventor 毛琳杨大伟许烨豪
Owner DALIAN NATIONALITIES UNIVERSITY