Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (8): 2338-2347.doi: 10.13229/j.cnki.jdxbgxb.20221373

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Three-dimensional vehicle multi-target tracking based on trajectory optimization

Hua CAI1(),Ting-ting KOU1,2,Yi-ning YANG3(),Zhi-yong MA4,Wei-gang WANG4,Jun-xi SUN5   

  1. 1.School of electronic information engineering, Changchun University of Science and Technology, Changchun 130022, China
    2.Industrial College of Artificial Intelligence, Changchun University of Architecture, Changchun 130604, China
    3.National Key Laboratory of Electromagnetic Space Security, Tianjin 300308, China
    4.No. 2 Department of Urology, the First Hospital of Jilin University, Changchun 130061, China
    5.School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
  • Received:2022-10-26 Online:2024-08-01 Published:2024-08-30
  • Contact: Yi-ning YANG E-mail:caihua@cust.edu.cn;yangyining53@cetc.com.cn

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Abstract:

In order to solve the problem of poor tracking effect of multi-target tracking algorithm in the case of occlusion， a multi-target tracking algorithm based on 3D point cloud detection is proposed. The 3D target detector based on point cloud is used to detect the vehicle target and obtain the location information of the 3D target； The target position in the next frame is predicted by tracking the target position in the current frame through a three-dimensional Kalman filter； The intersection ratio of 3D center point space distance and cross-union ratio of bird's eye view is fused as the weight， and the improved Hungarian algorithm is used for data association； Aiming at the problem of label switching before and after occlusion， a trajectory optimization algorithm is proposed. Experiments were conducted on KITTI dataset， and the vehicle tracking accuracy and tracking accuracy reached 84.71% and 86.63% respectively. Under the same threshold， this method is 6.28% and 0.39% higher than AB3DMOT respectively. Experimental results show that this algorithm can effectively improve the performance of 3D multi-target tracking.

Key words: computer version, multi-target tracking, 3D Kalman filter, trajectory optimization, improved Hungarian algorithm

CLC Number: 

  • TP391.4

Fig.1

Overall structure diagram of 3D target detection"

Fig.2

BEV-IOU"

Fig.3

Car tracking results under occlusion"

Fig.4

Algorithm flow of this paper"

Table 1

Ablation experimental results on KITTI tracking verification set"

算法匹配标准sAMOTA↑AMOTA↑AMOTP↑MOTA↑MOTP↑ID_SW↓
3DIOU+3D卡尔曼滤波IOUthres=0.2593.2845.4377.4186.2478.430
BEV-IOU+3D卡尔曼滤波IOUthres=0.2593.0346.2284.4385.0084.381
BEV-IOU+2D卡尔曼滤波IOUthres=0.2591.7844.8876.9585.2878.69

BEV-IOU+3D卡尔曼滤波+

三维中心点空间距离

IOUthres=0.2593.5546.8384.6186.4184.681
本文IOUthres=0.2593.7446.9984.7486.6384.710

Table 2

Validation sets of different algorithms on KITTI datasets quantified 3D tracking results"

算法输入数据匹配标准sAMOTA↑AMOTA↑AMOTP↑MOTA↑MOTP↑ID_SW↓FPS↑
mmMOT2D+3DIOUthres=0.2570.6133.0872.4574.0778.16104.8（GPU）
IOUthres=0.763.9124.9167.3251.9180.7124
FANTrack2D+3DIOUthres=0.2582.9740.0375.0174.3075.243525（GPU）
IOUthres=0.762.7224.7166.0649.1979.0138
FLOWMOT3DIOUthres=0.2590.5643.5176.0885.1379.371-
IOUthres=0.773.2929.5167.0662.6782.251
AB3DMOT3DIOUthres=0.2593.2845.4377.4186.2478.430207.4（CPU）
IOUthres=0.769.8127.2667.0057.0682.430
本文算法3DIOUthres=0.2593.7446.9984.7486.6384.71086.2（CPU）
IOUthres=0.774.8432.7674.0465.8387.810

Fig 5

Tracking performance of each algorithm is marked by MOTA and MOTP"

Fig.6

Visual display results"

Fig. 7

Comparison of visual tracking effects of two algorithms"

Fig. 8

Tracking effect under occlusion"

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