Multi-target identification tracking method for road traffic scene

Abstract

The invention discloses a multi-target identification tracking method for a road traffic scene. According to the method, the road traffic field environment and obstacle characteristics are combined. Dynamic obstacles around a road site are detected through a laser radar. The method comprises the following steps of: firstly, preprocessing laser radar data and converting the data into a binary image, secondly, extracting a dynamic obstacle by using a background difference method, carrying out clustering analysis, further carrying out classification recognition by using a Point CNN algorithm, obtaining pose transformation of the obstacle by using an iterative nearest neighbor algorithm, and finally, carrying out tracking by using a UKF for obstacle classification. The multi-target identification tracking method provided by the invention has good real-time performance, identification precision and tracking precision, and can effectively realize safety monitoring of the road traffic scene.

Description

technical field [0001] The invention relates to the field of road traffic safety, in particular to a multi-target identification and tracking method for road traffic scenes. Background technique [0002] With the development of economy and society, the problem of road traffic safety has become increasingly prominent. In road traffic accidents and secondary accidents in the rescue process, the proportion of cases caused by blind spots in vision is very large, which seriously endangers the safety of public life and property. For the prevention of traffic accidents, roadside perception units (such as 3D lidar) can be arranged to monitor road conditions in real time to prevent traffic accidents, such as figure 2 As shown; for the road rescue scene, the on-board perception unit (such as 3D lidar) can be used to monitor the safety of the road rescue scene, thereby reducing the possibility of secondary accidents, such as image 3 shown. [0003] At present, the environmental mon...

AI Technical Summary

Problems solved by technology

However, such traditional methods have the following problems: the traditional method of manually extracting features for object recognition and classification only extracts features from limited angles such as curvature and normal vectors, and cannot fully utilize all the information of the 3D point cloud, and these features are only useful for a certain Some specific transformations have invariance, poor generalization ability, and it is difficult to accurately obtain the category of obstacles; the lack of obstacle point clouds caused by occlusion, etc., makes the cluster centroids unable to accurately replace the obstacle centroids, resulting in reduced tracking accuracy

Therefore, this method cannot be applied to the above two road traffic scenarios

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