Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 27-33.doi: 10.6040/j.issn.1672-3961.0.2019.412

• Machine Learning & Data Mining • Previous Articles     Next Articles

Vehicle classification and tracking for complex scenes based on improved YOLOv3

Shiqi SONG(),Yan PIAO*(),Zexin JIANG   

  1. College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, Jilin, China
  • Received:2019-07-22 Online:2020-04-20 Published:2020-04-16
  • Contact: Yan PIAO E-mail:songsq_email@163.com;piaoyan@cust.edu.cn
  • Supported by:
    国家自然科学基金资助项目(60977011);吉林省科技发展项目(20180623039TC)

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Abstract:

Aiming at the influence of weather conditions and mutual occlusion of vehicles on vehicle classification and tracking accuracy and stability, a hybrid model based on improved YOLOv3 and matching tracking was proposed. The improved YOLOv3 network refered to DenseNet′s design idea, replaced the residual layer in the network with a dense convolution block and changed the design structure of the network. The fused features of dense convolution blocks and convolution layers were classified by using Softmax classifier. According to the detection result of single frame image, the target matching function was designed to solve the vehicle tracking problem in video sequence. In the KITTI dataset test, the improved algorithm achieved an average precision of 93.01%, the number of frames per second reached 48.98, and the average recognition rate in the self-built dataset was 95.79%. The experimental results showed that the proposed method could effectively distinguish the types of vehicles in complex scenes with higher accuracy. At the same time, the method had higher accuracy and robustness in vehicle tracking.

Key words: image processing, vehicle classification, convolutional neural network, YOLOv3, match tracking

CLC Number: 

  • TP391

Fig.1

Improved YOLOv3 network structure"

Fig.2

Dense block structure diagram"

Fig.3

Dense convolutional network feature extraction structure"

Table 1

Different methods test results in public data sets"

方法 平均准确率均值/% 传输速率/(帧·s-1)
R-CNN 52.76 0.38
Fast R-CNN 61.53 0.54
SSD 80.01 51.26
YOLO 56.78 67.43
YOLOv3 90.76 48.99
本文 93.01 48.98

Fig.4

PR curves of different methods in public data sets"

Fig.5

Vehicle classification results in different scenes"

Table 2

Different scenes classification results  %"

方法 场景 目标分类率 误检率 漏检率
Fast R-CNN 白天 77.78 11.56 10.66
YOLO 白天 73.26 19.75 6.99
YOLOv3 白天 92.41 6.46 1.13
本文 白天 97.03 2.91 0.06
Fast R-CNN 夜晚 71.67 13.15 15.18
YOLO 夜晚 68.50 17.28 14.22
YOLOv3 夜晚 87.98 7.63 4.39
文本 夜晚 94.76 4.09 1.15
Fast R-CNN 雨天 77.54 11.98 10.48
YOLO 雨天 72.33 19.47 8.20
YOLOv3 雨天 91.89 6.78 1.33
本文 雨天 96.73 2.03 1.24
Fast R-CNN 雾天 75.02 12.83 12.15
YOLO 雾天 71.32 16.84 11.84
YOLOv3 雾天 89.57 7.55 2.88
本文 雾天 94.71 3.69 1.60
Fast R-CNN 雪天 75.54 11.98 12.48
YOLO 雪天 70.33 18.47 11.20
YOLOv3 雪天 90.89 7.78 1.33
本文 雪天 95.73 3.03 1.24

Fig.6

Vehicle tracking results in different scenes"

Table 3

Video sequence tracking results  %"

方法 成功率 精确率
CSK 54.5 39.8
TLD 55.8 43.7
ALSA 53.2 42.4
本文 55.5 45.6
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