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Vehicle-mounted terminal multi-target identification tracking prediction method

A prediction method and multi-target technology, applied in the field of multi-target recognition, tracking and prediction on the vehicle side, can solve the problems of increasing system implementation costs, difficulty in greatly improving the performance of assisted driving technology, complexity, etc., to achieve rapid deployment and fast training speed , the effect of small model size

Active Publication Date: 2021-02-02
GUILIN UNIV OF ELECTRONIC TECH +1
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  • Description
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AI Technical Summary

Problems solved by technology

Due to the extremely complex actual road conditions, it is difficult to greatly improve the performance of assisted driving technology based on traditional target detection
Existing autonomous driving technologies, although they can rely on advanced radar systems to compensate, greatly increase the cost of system implementation

Method used

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  • Vehicle-mounted terminal multi-target identification tracking prediction method
  • Vehicle-mounted terminal multi-target identification tracking prediction method
  • Vehicle-mounted terminal multi-target identification tracking prediction method

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Embodiment Construction

[0036] Below in conjunction with specific case the present invention is described further:

[0037] Such as figure 1 As shown, a vehicle-side multi-target recognition tracking prediction method, the method is a vehicle-side multi-target recognition tracking prediction method based on the fusion of YOLOv5s and FairMOT, including the following steps:

[0038] (1) YOLOv5s algorithm multi-target recognition model training in traffic scenarios

[0039] ①The establishment and marking of the traffic scene data set, through the shooting of a large number of pictures in the actual scene and the collection of surveillance video in the road traffic scene, use lablimg to mark the sample image, and use VOTT (Visual ObjectTagging Tool) to mark the video data , it should be pointed out that these two types of marking software are essentially marking a single image. The difference is that the video marking software has functions such as screening sample images and rough automatic marking bas...

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Abstract

The invention discloses a vehicle-mounted terminal multi-target recognition tracking prediction method, which is a vehicle-mounted terminal multi-target recognition tracking prediction method based onYOLOv5s (You Only Look Once v5s) and FairMOT (FairMOT) fusion, and detects vehicles in front of a road quickly and accurately in real time by using a YOLOv5s deep learning object detection technology. The YOLOv5s model is fused into the FairMOT architecture detection module to perform target detection and re-identification tracking in a single network so as to realize position detection, type identification and multi-target motion trajectory tracking of the traffic target in front of the vehicle on the road, thereby achieving prediction of driving behaviors of lane change, following, deceleration and the like of the traffic target in front of the vehicle.

Description

technical field [0001] The invention belongs to the field of automatic driving in intelligent transportation. In the case of mixed traffic, the automatic driving vehicle needs to quickly and accurately identify and track the vehicles, pedestrians, obstacles and other targets in front, so as to realize the lane change and car-following of the targets ahead. , deceleration and other driving behavior prediction problems, a vehicle-side multi-target recognition tracking prediction method based on the fusion of YOLOv5s and FairMOT is proposed. Background technique [0002] In recent years, autonomous driving with the aim of greatly reducing accidents has gradually become a social need. The combination of automotive preventive safety technology and object detection algorithms has developed into an autonomous driving assistance technology that can automate part of car driving performed by humans and is expected to further improve the safety of car driving. In autonomous driving te...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N5/04G06T5/50
CPCG06T5/50G06N5/04G06T2207/20081G06T2207/30252G06V40/103G06V20/56G06N3/045Y02T10/40
Inventor 万千刘华磊彭国庆郑钰谢振友
Owner GUILIN UNIV OF ELECTRONIC TECH
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