August & September  2019, 12(4&5): 1117-1133. doi: 10.3934/dcdss.2019077

Research on regional clustering and two-stage SVM method for container truck recognition

1. 

Container Supply Chain Tech. Engineering Research Center, Shanghai Maritime University, China

2. 

Shanghai Weiguo Port Equipment Company Ltd, China

3. 

Shanghai Abian Smart Technology Company Ltd, China

4. 

Shanghai Haizhu Smart Technology Company Ltd, China

5. 

Institute of Logistics Science & Engineering, Shanghai Maritime University, China

6. 

ISCTE-IUL, Inst Telecomunicacoes, Lisbon, Portugal

* Corresponding author: Chao Mi

Received  June 2017 Revised  December 2017 Published  November 2018

With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.

Citation: Chao Mi, Jun Wang, Weijian Mi, Youfang Huang, Zhiwei Zhang, Yongsheng Yang, Jun Jiang, Postolache Octavian. Research on regional clustering and two-stage SVM method for container truck recognition. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1117-1133. doi: 10.3934/dcdss.2019077
References:
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Y. Cao, G. I. Kirilova and M. L. Grunis, Cooperative research projects of master's students (education programs) in the open informational educational environment, Eurasia Journal of Mathematics Science and Technology Education, 13 (2017), 2859.

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F. Garcia, D. Martin and A. D. L. Escalera, et al., Sensor Fusion Methodology for Vehicle Detection. IEEE Intelligent Transportation Systems Magazine, 9 (2017), 123-133.

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M. JalalatM. Nejati and A. Majidi, Vehicle detection and speed estimation using cascade classifier and sub-pixel stereo matching, Signal Processing and Intelligent Systems (ICSPIS), (2016), 1-5. 

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Q. Jiang, L. Cao and M. Cheng, et al., Deep neural networks-based vehicle detection in satellite images, International Symposium on Bioelectronics and Bioinformatics, (2015), 184-187.

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S. Kamkar and R. Safabakhsh, Vehicle detection, counting and classification in various conditions, IET Intelligent Transport Systems, 10 (2016), 406-413. 

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H. Kuang, X. Zhang and Y. J. Li, et al., Nighttime vehicle detection based on bio-inspired image enhancement and weighted score-level feature fusion, IEEE Transactions on Intelligent Transportation Systems, PP (2017), 1-10.

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B. Li, T. Zhang and T. Xia, Vehicle Detection From 3D Lidar Using Fully Convolutional Network, arXiv preprint, 2016, arXiv: 1608.07916.

[11]

C. Mi, Y. Shen and W. Mi, et al., Ship identification algorithm based on 3d point cloud for automated ship loaders, Journal of Coastal Research, 73 (2015), 28-34.

[12]

C. Mi, Y. Shen and W. Mi, et al., Ship identification algorithm based on 3d point cloud for automated ship loaders, Journal of Coastal Research, 73 (2015), 28-34.

[13]

C. Mi, Z. Zhang and Y. Huang, et al., a fast automated vision system for container corner casting recognition, Journal of Marine Science & Technology, 24 (2016), 54-60.

[14]

J. Mrovlje and D. Vran, Automatic detection of the truck position using stereoscopy, IEEE International Conference on Industrial Technology, (2012), 755-759. 

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S A. Nur, M. M. Ibrahim and N. M. Ali, et al., Vehicle detection based on underneath vehicle shadow using edge features, Control System, Computing and Engineering (ICCSCE), (2016), 407-412.

[16]

Y. ShenW. Mi and Z. Zhang, A positioning lockholes of container corner castings method based on image recognition, Polish Maritime Research, 24 (2017), 95-101. 

[17]

Y. Shen, N. Zhao and M. Xia, et al., A deep q-learning network for ship stowage planning problem, Polish Maritime Research, 24 (2017), 102-109.

[18]

L. Si and H. Qiao, Performance of Financial Expenditure in China's basic science and math education: Panel data analysis based on CCR model and BBC model, EURASIA Journal of Mathematics Science and Technology Education, 13 (2017), 5217-5224. 

[19]

L. W. SommerT. Schuchert and J. Beyerer, A comprehensive study on object proposals methods for vehicle detection in aerial images, Pattern Recogniton in Remote Sensing (PRRS), (2016), 1-6. 

[20]

T. Tangkocharoen and A. Srisuphab, Vehicle detection on a pint-sized computer, Knowledge and Smart Technology (KST), (2017), 40-44. 

[21]

X. Wang, L. Xu and H. Sun, et al., On-road vehicle detection and tracking using mmw radar and monovision fusion, IEEE Transactions on Intelligent Transportation Systems, 17 (2016), 1-10.

[22]

Q. Wei and B. Yang, Adaptable vehicle detection and speed estimation for changeable urban traffic with anisotropic magnetoresistive sensors, IEEE Sensors Journal, 17 (2017), 2021-2028. 

[23]

J. Yuan, W. Cheng and L. Sun, et al., A constructing vehicle intrusion detection algorithm based on BOW presentation model, Signal and Image Processing (ICSIP), (2016), 183-188.

[24]

Z. ZhangC. Xu and W. Feng, Road vehicle detection and classification based on Deep Neural Network, Software Engineering and Service Science (ICSESS), (2016), 675-678. 

[25]

Y. Zhang, C. Zhao and J. He, et al., Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement, IET Intelligent Transport Systems, 10 (2016), 445-452.

[26]

Z. Zhang, X. Yu and F. You, et al., A front vehicle detection algorithm for intelligent vehicle based on improved gabor filter and SVM, Recent Patents on Computer Science, 8 (2015), 32-40.

[27]

Y. ZhangW. Chen and H. Liu, Design for the competitiveness assessment index system of aviation aerospace manufacture industry on basis of GEMPearsonVC combination method, Journal of Interdisciplinary Mathematics, 20 (2017), 255-268. 

[28]

H. Zhu and F. Yu, A cross-correlation technique for vehicle detections in wireless magnetic sensor network, IEEE Sensors Journal, 16 (2016), 1-1. 

show all references

References:
[1]

Y. Cai, X. Chen and H. Wang, et al., Deep representation and stereo vision based vehicle detection, IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems IEEE, (2015), 305-310.

[2]

Y. Cao, G. I. Kirilova and M. L. Grunis, Cooperative research projects of master's students (education programs) in the open informational educational environment, Eurasia Journal of Mathematics Science and Technology Education, 13 (2017), 2859.

[3]

F. Garcia, D. Martin and A. D. L. Escalera, et al., Sensor Fusion Methodology for Vehicle Detection. IEEE Intelligent Transportation Systems Magazine, 9 (2017), 123-133.

[4]

I. Iszaidy, A. Alias and R. Ngadiran, et al., Video size comparison for embedded vehicle speed detection & travel time estimation system by using Raspberry Pi, Robotics, Automation and Sciences (ICORAS), (2016), 1-4.

[5]

M. JalalatM. Nejati and A. Majidi, Vehicle detection and speed estimation using cascade classifier and sub-pixel stereo matching, Signal Processing and Intelligent Systems (ICSPIS), (2016), 1-5. 

[6]

Q. Jiang, L. Cao and M. Cheng, et al., Deep neural networks-based vehicle detection in satellite images, International Symposium on Bioelectronics and Bioinformatics, (2015), 184-187.

[7]

S. Kamkar and R. Safabakhsh, Vehicle detection, counting and classification in various conditions, IET Intelligent Transport Systems, 10 (2016), 406-413. 

[8]

H. Kuang, X. Zhang and Y. J. Li, et al., Nighttime vehicle detection based on bio-inspired image enhancement and weighted score-level feature fusion, IEEE Transactions on Intelligent Transportation Systems, PP (2017), 1-10.

[9]

W. Li, W. Liu and X. Xu, et al., The port service ecosystem research based on the lotkavolterra model[J], Polish Maritime Research, 24 (2017), 86-94.

[10]

B. Li, T. Zhang and T. Xia, Vehicle Detection From 3D Lidar Using Fully Convolutional Network, arXiv preprint, 2016, arXiv: 1608.07916.

[11]

C. Mi, Y. Shen and W. Mi, et al., Ship identification algorithm based on 3d point cloud for automated ship loaders, Journal of Coastal Research, 73 (2015), 28-34.

[12]

C. Mi, Y. Shen and W. Mi, et al., Ship identification algorithm based on 3d point cloud for automated ship loaders, Journal of Coastal Research, 73 (2015), 28-34.

[13]

C. Mi, Z. Zhang and Y. Huang, et al., a fast automated vision system for container corner casting recognition, Journal of Marine Science & Technology, 24 (2016), 54-60.

[14]

J. Mrovlje and D. Vran, Automatic detection of the truck position using stereoscopy, IEEE International Conference on Industrial Technology, (2012), 755-759. 

[15]

S A. Nur, M. M. Ibrahim and N. M. Ali, et al., Vehicle detection based on underneath vehicle shadow using edge features, Control System, Computing and Engineering (ICCSCE), (2016), 407-412.

[16]

Y. ShenW. Mi and Z. Zhang, A positioning lockholes of container corner castings method based on image recognition, Polish Maritime Research, 24 (2017), 95-101. 

[17]

Y. Shen, N. Zhao and M. Xia, et al., A deep q-learning network for ship stowage planning problem, Polish Maritime Research, 24 (2017), 102-109.

[18]

L. Si and H. Qiao, Performance of Financial Expenditure in China's basic science and math education: Panel data analysis based on CCR model and BBC model, EURASIA Journal of Mathematics Science and Technology Education, 13 (2017), 5217-5224. 

[19]

L. W. SommerT. Schuchert and J. Beyerer, A comprehensive study on object proposals methods for vehicle detection in aerial images, Pattern Recogniton in Remote Sensing (PRRS), (2016), 1-6. 

[20]

T. Tangkocharoen and A. Srisuphab, Vehicle detection on a pint-sized computer, Knowledge and Smart Technology (KST), (2017), 40-44. 

[21]

X. Wang, L. Xu and H. Sun, et al., On-road vehicle detection and tracking using mmw radar and monovision fusion, IEEE Transactions on Intelligent Transportation Systems, 17 (2016), 1-10.

[22]

Q. Wei and B. Yang, Adaptable vehicle detection and speed estimation for changeable urban traffic with anisotropic magnetoresistive sensors, IEEE Sensors Journal, 17 (2017), 2021-2028. 

[23]

J. Yuan, W. Cheng and L. Sun, et al., A constructing vehicle intrusion detection algorithm based on BOW presentation model, Signal and Image Processing (ICSIP), (2016), 183-188.

[24]

Z. ZhangC. Xu and W. Feng, Road vehicle detection and classification based on Deep Neural Network, Software Engineering and Service Science (ICSESS), (2016), 675-678. 

[25]

Y. Zhang, C. Zhao and J. He, et al., Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement, IET Intelligent Transport Systems, 10 (2016), 445-452.

[26]

Z. Zhang, X. Yu and F. You, et al., A front vehicle detection algorithm for intelligent vehicle based on improved gabor filter and SVM, Recent Patents on Computer Science, 8 (2015), 32-40.

[27]

Y. ZhangW. Chen and H. Liu, Design for the competitiveness assessment index system of aviation aerospace manufacture industry on basis of GEMPearsonVC combination method, Journal of Interdisciplinary Mathematics, 20 (2017), 255-268. 

[28]

H. Zhu and F. Yu, A cross-correlation technique for vehicle detections in wireless magnetic sensor network, IEEE Sensors Journal, 16 (2016), 1-1. 

Figure 1.  A quay crane was unloading container from a truck
Figure 2.  Cameras' positions on the quay crane
Figure 3.  The field installation of the camera
Figure 4.  Truck detection algorithm flowchart
Figure 5.  Schematic illustration of background extraction and update
Figure 6.  The result of background subtraction
Figure 7.  Regional clustering of image patches
Figure 8.  Outer rectangle of the cluster
Figure 9.  Schematic illustration of the HOG descriptor
Figure 10.  The HOG features of trucks in different position
Figure 11.  Schematic illustration of two-stage SVM classifier
Figure 12.  Two-stage SVM algorithm flowchart
Figure 13.  The sample sets of two kinds of truck shape features
Figure 14.  Results fusion
Figure 15.  The experimental results of truck detection
Figure 16.  Comparison of two algorithms about average processing time
Table 1.  Experimental results
The fast truck detection algorithm Traditional SVM
Algorithm time Number TRUE FALSE Accuracy APT
(ms)
TRUE FALSE Accuracy APT
(ms)
Test Set 1 day 50 50 0 100% 185.4 50 0 100% 423.6
Test Set 2 night 50 48 2 96% 135.4 46 2 92% 392.3
Test Set 3 day 50 50 0 100% 133.1 48 5 96% 404.8
Test Set 4 night 50 47 3 94% 107.4 47 1 94% 384.3
Test Set 5 day 50 49 1 98% 236.5 48 6 96% 419.9
Test Set 6 night 50 50 0 100% 201.0 46 2 92% 424.9
Test Set 7 day 50 50 0 100% 125.4 49 6 98% 381.8
Test Set 8 night 50 49 1 98% 103.1 45 4 90% 406.8
Test Set 9 day 50 50 0 100% 124.2 50 1 100% 359.3
Test Set 10 night 50 50 0 100% 105.4 48 3 96% 380.6
Test Set 11 day 50 50 0 100% 113.2 48 30 96% 365.8
Test Set 12 night 50 47 3 94% 245.1 46 4 92% 401.3
Test Set 13 day 50 49 1 98% 135.2 50 0 100% 502.8
Test Set 14 night 50 48 2 96% 116.3 45 5 90% 396.7
Test Set 15 day 50 50 0 100% 127.8 50 0 100% 456.5
Test Set 16 night 50 50 0 100% 156.5 46 4 92% 379.8
Test Set 17 day 50 50 0 100% 195.2 49 1 98% 427.8
Test Set 18 night 50 48 2 96% 188.3 42 8 84% 511.3
Test Set 19 day 50 50 0 100% 152.3 47 3 94% 386.5
Test Set 20 night 50 46 4 92% 164.5 44 6 88% 462.4
sum 1000 981 19 98% 152.565 944 91 94% 413.46
The fast truck detection algorithm Traditional SVM
Algorithm time Number TRUE FALSE Accuracy APT
(ms)
TRUE FALSE Accuracy APT
(ms)
Test Set 1 day 50 50 0 100% 185.4 50 0 100% 423.6
Test Set 2 night 50 48 2 96% 135.4 46 2 92% 392.3
Test Set 3 day 50 50 0 100% 133.1 48 5 96% 404.8
Test Set 4 night 50 47 3 94% 107.4 47 1 94% 384.3
Test Set 5 day 50 49 1 98% 236.5 48 6 96% 419.9
Test Set 6 night 50 50 0 100% 201.0 46 2 92% 424.9
Test Set 7 day 50 50 0 100% 125.4 49 6 98% 381.8
Test Set 8 night 50 49 1 98% 103.1 45 4 90% 406.8
Test Set 9 day 50 50 0 100% 124.2 50 1 100% 359.3
Test Set 10 night 50 50 0 100% 105.4 48 3 96% 380.6
Test Set 11 day 50 50 0 100% 113.2 48 30 96% 365.8
Test Set 12 night 50 47 3 94% 245.1 46 4 92% 401.3
Test Set 13 day 50 49 1 98% 135.2 50 0 100% 502.8
Test Set 14 night 50 48 2 96% 116.3 45 5 90% 396.7
Test Set 15 day 50 50 0 100% 127.8 50 0 100% 456.5
Test Set 16 night 50 50 0 100% 156.5 46 4 92% 379.8
Test Set 17 day 50 50 0 100% 195.2 49 1 98% 427.8
Test Set 18 night 50 48 2 96% 188.3 42 8 84% 511.3
Test Set 19 day 50 50 0 100% 152.3 47 3 94% 386.5
Test Set 20 night 50 46 4 92% 164.5 44 6 88% 462.4
sum 1000 981 19 98% 152.565 944 91 94% 413.46
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