Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 28-34.doi: 10.6040/j.issn.1672-3961.0.2019.179

• Control Science & Engineering • Previous Articles     Next Articles

Risk assessment method based on spatial hidden danger distribution and motion intention analysis

Yuenan ZHAO1(),Guiyou CHEN1,*(),Chen SUN1,Ning LU1,Liwei LIAO2   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
    2. School of Software and Microelectronics, Peking University, Beijing 100871, China
  • Received:2019-04-19 Online:2020-02-20 Published:2020-02-14
  • Contact: Guiyou CHEN E-mail:201734497@mail.sdu.edu.cn;chenguiyou@sdu.edu.cn
  • Supported by:
    山东省重点研发计划项目(2017GGX90105);山东省自然科学基金(ZR2017MF014)

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Abstract:

To solve problems of slow detection and lack of behavioral safety analysis in intelligent nursing, a risk assessment method based on spatial hidden danger distribution and motion intention analysis was proposed. The k-means++ algorithm and background elimination method were used to enhance the object detection capability of YOLO(you only look once) v3, which led to the classification and localization of human targets as well as dangerous objects. The Kalman filter was used to predict the moving trajectory, where two parameters, namely the deviation angle of motion and the distance between the human target and danger, were used to construct the human movement patterns. The risk assessment model was established, and the degree of danger was evaluated according to different movement behavior patterns. Experimental results showed that for identifying different objects in the test set, both the detection precision and the recall rate of the enhanced YOLOv3 algorithm were over 95%. An increasement of IOU(intersection over union) at 7% was witnessed, and frames rate reached 31.3 frames/s. These results proved the real-time performance of the system. Since the proposed risk assessment model incorporated motion intentions of the human target, this method was expected to boost the performance in fitting the risk progression of different movement patterns, making the risk assessment more reasonable.

Key words: intelligent surveillance, YOLOv3, Kalman filter, spatial hidden danger, motion intention, risk assessment

CLC Number: 

  • TP391.4

Fig.1

Structure of YOLOv3"

Fig.2

Algorithm workflow"

Fig.3

Analysis of clustering effect"

Fig.4

Object detection of improved YOLOv3"

Fig.5

Boxplot of IOU"

Table 1

Comparison of different algorithms frameworks"

不同框架 平均IOU 精度 召回率 帧率/(帧·s-1)
YOLOv3-416[15] 0.71 0.95 0.95 33.4
SSD-321[13] 0.65 0.89 0.91 16.4
Faster-RCNN[12] 0.80 0.94 0.95 6.6
Casc-RCNN[20] 0.82 0.96 0.96 8.0
增强YOLOv3-416 0.78 0.96 0.97 31.3

Fig.6

Comparison of real and predicted tracks"

Fig.7

Impact of parameter kd"

Fig.8

Impact of parameter σ"

Table 2

The change of risk in 3 different interactive modes"

危险情形 d/pix θ/° R/%
Cl 410. 17 32. 35 34.04
C2 375. 20 23. 83 40. 90
C3 339. 27 22. 63 48. 09
C4 293.44 14.37 58.08
C5 243.51 0. 16 69. 03
C6 186. 98 23. 48 79. 27
Cl 143. 70 7.91 87. 75
C8 104. 97 9.60 93. 13
A1 149. 29 114. 18 62.80
A2 130. 50 126. 43 60.29
A3 158. 10 148. 30 49. 36
A4 187.02 126. 88 53.74
A5 216. 72 144. 73 44. 17
A6 251.81 125. 33 45.43
A7 238. 58 176. 94 27. 66
A8 303. 30 128. 75 37. 18
B1 292.49 44. 24 55.79
B2 262.21 42.09 62. 25
B3 231.67 49. 05 67.33
B4 204. 43 59. 19 70. 55
B5 183.45 66. 42 72. 57
B6 168.08 79.21 71.64
B7 159.23 98. 25 67.05
B8 163. 83 111.08 62. 11
B9 182. 95 125. 36 54.77
BIO 219. 62 141.77 44. 76
Bll 256. 68 146. 49 38. 74
B12 284. 98 164. 52 30. 60

Fig.9

Warning of dangerous motion pattern"

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