doi: 10.3934/dcdss.2020269

A fast Human detection algorithm for container reach stackers

1. 

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

2. 

Logistics & Engineering College, Shanghai Maritime University, China

3. 

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

4. 

Shanghai SMU-Vision Technology Co., Ltd, China

*Corresponding author: Weijian Mi

Received  May 2019 Revised  May 2019 Published  February 2020

With the development of automation in the ports, more and more attention has been paid to the effective real-time monitoring of personnel intrusion into the unmanned area in the process of operation. In this paper, a fast human detection algorithm is proposed. Firstly, by improving HOG algorithm flow, the speed of feature extraction of HOG is greatly accelerated. Then, based on the HOG features, a 2-stage classifier based on Adaboost is proposed, which trains the front/back and side of the human sample library, so that the algorithm can adapt to the multi-pose human shape detection under the complex ports backgrounds. Finally, this paper presented a group of experiments about human detection on the container reach stacker of Shanghai International Port(Group) YIDONG Container Terminal Branch. The results show that the recognition accuracy of the combined algorithm of fast HOG and 2-stage classifier can reach 95% , and the detection time can be within 150ms. It realizes the calculation of 5-channel video human detection at the same time in a small embedded board, and meets the security requirements of unmanned areas in the ports.

Citation: Chao Mi, Mengtong Wu, Weijian Mi, Jun Wang, Jun Jiang, Zhiwei Zhang. A fast Human detection algorithm for container reach stackers. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020269
References:
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show all references

References:
[1]

M. BertozziA. BroggiA. Lasagni and M. Rose, Infrared stereo vision-based pedestrian detection, IEEE Proceedings on Intelligent Vehicles Symposium, 2005 (2005), 24-29.  doi: 10.1109/IVS.2005.1505072.  Google Scholar

[2]

L. BiO. Tsimhoni and Y. Liu, Using image-based metrics to model pedestrian detection performance with night-vision systems, IEEE Transactions on Intelligent Transportation Systems, 10 (2009), 155-164.   Google Scholar

[3]

E. C. C. Favi, A 17ps time-to-digital converter implemented in 65nm fpga technology, Acm/sigda International Symposium on Field Programmable Gate Arrays, 2009 (2009), 113-120.  doi: 10.1145/1508128.1508145.  Google Scholar

[4]

J. W. C. Mi and W. Mi, Research on regional clustering and two-stage svm method for container truck recognition, Discrete and Continuous Dynamical Systems Series S, 12 (2019), 1117-1133.   Google Scholar

[5]

X. H. C. Mi and H. Liu, Research on a fast human-detection algorithm for unmanned surveillance area in bulk ports, Mathematical Problems in Engineering, 2014 (2014), 1-17.  doi: 10.1155/2014/386764.  Google Scholar

[6]

Z. Z. C. Mi and X. He, Two-stage classification approach for human detection in camera video in bulk ports, Polish Maritime Research, 22 (2015), 163-170.  doi: 10.1515/pomr-2015-0049.  Google Scholar

[7]

L. ChaoX. Wang and W. Liu, Neural features for pedestrian detection, Neurocomputing, 238 (2017), 420-432.   Google Scholar

[8]

M. ChaoS. YangW. Mi and Y. Huang, Ship identification algorithm based on 3d point cloud for automated ship loaders, Journal of Coastal Research, 73 (2015), 28-34.   Google Scholar

[9]

M. M. ChengZ. ZhangW. Y. Lin and P. Torr, Bing: Binarized normed gradients for objectness estimation at 300fps, IEEE Computer Vision and Pattern Recognition, 2014 (2014), 3286-3293.  doi: 10.1109/CVPR.2014.414.  Google Scholar

[10]

T. J. B. D. A. Danaher and J. K. Ball, Operation of the eaton vorad collision warning system and analysis of the recorded data, SAE Paper, 2009 (2009), 2911. doi: 10.4271/2009-01-2911.  Google Scholar

[11]

T. Fujioka and K. Suzuki, Control of longitudinal and lateral platoon using sliding control, Vehicle System Dynamics, 23 (1994), 647-664.  doi: 10.1080/00423119408969079.  Google Scholar

[12]

V. Gaikwad and S. Lokhande, Vision based pedestrian detection for advanced driver assistance, Procedia Computer Science, 46 (2015), 321-328.  doi: 10.1016/j.procs.2015.02.027.  Google Scholar

[13]

P. Govardhan, Night time pedestrian detection for advanced driving assistance systems (adas) using near infrared images, Rourkela: National Institute of Technology, 2014 (2014), 1-44.   Google Scholar

[14]

M. Y. H. Harraga, Attractors for a nonautonomous reaction-diffusion equation with delay, Applied Mathematics and Nonlinear Sciences, 3 (2018), 127-150.  doi: 10.21042/AMNS.2018.1.00010.  Google Scholar

[15]

M. A. J. P. Ruiz-Fernández and J. Benlloch, Influence of seasonal factors in the earned value of construction, Applied Mathematics and Nonlinear Sciences, 4 (2019), 21-34.   Google Scholar

[16]

A. LakshmiA. G. J. Faheema and D. Deodhare, Pedestrian detection in thermal images: An automated scale based region extraction with curvelet space validation, Infrared Physics and Technology, 76 (2016), 421-438.  doi: 10.1016/j.infrared.2016.03.012.  Google Scholar

[17]

Y. LiuL. ZouJ. LiJ. YanW. Shi and D. Deng, Segmentation by weighted aggregation and perceptual hash for pedestrian detection, Journal of Visual Communication and Image Representation, 36 (2016), 80-89.  doi: 10.1016/j.jvcir.2016.01.010.  Google Scholar

[18]

Z. ManZ. Sun and T. Tan, Deformed iris recognition using bandpass geometric features and lowpass ordinal features, IEEE International Conference on Biometrics, 2013 (2013), 1-6.   Google Scholar

[19]

H. Meinel, Applications of microwaves and millimeter waves for vehicle, IEEE Communications and Control in Europe, 1992 (1992), 609-612.   Google Scholar

[20]

Z. Mi C.and ZhangY. Huang and Y. Shen, A fast automated vision system for container corner casting recognition, Journal of Marine Science and Technology, 24 (2016), 54-60.   Google Scholar

[21]

S. MoonI. Moon and K. Yi, Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance, Control Engineering Practice, 17 (2009), 442-455.  doi: 10.1016/j.conengprac.2008.09.006.  Google Scholar

[22]

B. Triggs and N. Dalal, Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1 (2005), 886-893.  doi: 10.1109/CVPR.2005.177.  Google Scholar

[23]

D. OlmedaA. D. L. Escalera and J. M. Armingol, Detection and tracking of pedestrians in infrared images, IEEE Circuits and Systems, International Conference on Signals, 2009 (2009), 1-6.  doi: 10.1109/ICSCS.2009.5412297.  Google Scholar

[24]

D. OlmedaC. PremebidaU. NunesJ. M. Armingol and A. D. L. Escalera, Pedestrian detection in far infrared images, Integrated Computer-Aided Engineering, 20 (2013), 347-360.  doi: 10.3233/ICA-130441.  Google Scholar

[25]

W. Ouyang and X. Wang, Joint deep learning for pedestrian detection, IEEE International Conference on Computer Vision, 2013 (2013), 2056-2063.  doi: 10.1109/ICCV.2013.257.  Google Scholar

[26]

S. PaisitkriangkraiC. Shen and A. V. D. Hengel, Efficient pedestrian detection by directly optimize the partial area under the roc curve, IEEE International Conference on Computer Vision, 2013 (2013), 1057-1064.   Google Scholar

[27]

S. RenK. HeR. Girshick and J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (2017), 1137-1149.  doi: 10.1109/TPAMI.2016.2577031.  Google Scholar

[28]

A. RovidA. R. Varkonyi-KoczyM. D. G. RuanoN. Varlaki and P. Michelberger, Soft computing based car body deformation and ees determination for car crash analysis systems, IEEE Transactions on Instrumentation and Measurement, 55 (2006), 2304-2312.  doi: 10.1109/IMTC.2004.1351403.  Google Scholar

[29]

Y. K. T. Miyazaki, Multiple human tracking using binary infrared sensors, Sensors, 15 (2015), 13459-13476.  doi: 10.3390/s150613459.  Google Scholar

[30]

Y. L. W. Li and B. Fu, Cascade classifier using combination of histograms of oriented gradients for rapid pedestrian detection, Journal of Software, 8 (2013), 71-77.   Google Scholar

[31]

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

[32]

C. YangH. LiuS. Liao and S. Wang, Pedestrian detection in thermal infrared image using extreme learning machine, Springer International Publishing, 2 (2015), 31-40.  doi: 10.1007/978-3-319-14066-7_4.  Google Scholar

[33]

J. Yun and S. S. Lee, Human movement detection and identification using pyroelectric infrared sensors, Sensors, 14 (2014), 8057-8081.  doi: 10.3390/s140508057.  Google Scholar

Figure 1.  Container Reach Stacker
Figure 2.  Schema of Fast HOG Feature Extraction Algorithm
Figure 3.  Schema of HOG Features of Whole Graph
Figure 4.  The 2-stage classification approach flowchart
Figure 5.  The sample sets of F & B and side postures
Figure 6.  The Schema of Weak Classifier Decision Tree Model sets of F & B and side postures
Figure 7.  Detection Camera Installed on Container Reach Stacker
Figure 8.  Human Detection Result
Figure 9.  Human Detection Result
Figure 10.  Human Detection Result
Table 1.  Training sample library
Classifier Type and Number of the Sample Recognition rate
Adaboost (Preprocess) F & B:1400 Side: 6000 97.80%
Adaboost (F & B) F & B: 1400 Negative Sample: 6000 98.60%
Adaboost (Side) Side:1400 Negative Sample: 6000 94.73%
Classifier Type and Number of the Sample Recognition rate
Adaboost (Preprocess) F & B:1400 Side: 6000 97.80%
Adaboost (F & B) F & B: 1400 Negative Sample: 6000 98.60%
Adaboost (Side) Side:1400 Negative Sample: 6000 94.73%
Table 2.  The experimental results
Test set Number of person per sample set Classifier Detect Misdetect FALSE FR DR MDR DT
F & B 1352 F & B 1332 2 18 1.33% 98.52% 0.15% 100 ms
Side 634 699 19 1.41% 46.89% 51.70%
SVM 1278 17 57 4.22% 94.53% 1.26% 2000 ms
2-stage Adaboost 1315 11 26 1.92% 97.26% 0.81% 150 ms
Side 1250 F & B 719 477 54 4.32% 57.52% 38.16% 100 ms
Side 1198 23 29 2.32% 95.84% 1.84%
SVM 1102 56 92 7.36% 88.16% 4.48% 2000 ms
2-stage Adaboost 1197 22 31 2.48% 95.76% 1.76% 150 ms
Mix 1930 F & B 1463 371 96 4.97% 75.80% 19.22% 100 ms
Side 802 1025 103 5.34% 41.55% 53.11%
SVM 1783 48 99 5.13% 92.38% 2.49% 2000 ms
2-stage Adaboost 1860 4 66 3.42% 96.37% 0.21% 150 ms
Test set Number of person per sample set Classifier Detect Misdetect FALSE FR DR MDR DT
F & B 1352 F & B 1332 2 18 1.33% 98.52% 0.15% 100 ms
Side 634 699 19 1.41% 46.89% 51.70%
SVM 1278 17 57 4.22% 94.53% 1.26% 2000 ms
2-stage Adaboost 1315 11 26 1.92% 97.26% 0.81% 150 ms
Side 1250 F & B 719 477 54 4.32% 57.52% 38.16% 100 ms
Side 1198 23 29 2.32% 95.84% 1.84%
SVM 1102 56 92 7.36% 88.16% 4.48% 2000 ms
2-stage Adaboost 1197 22 31 2.48% 95.76% 1.76% 150 ms
Mix 1930 F & B 1463 371 96 4.97% 75.80% 19.22% 100 ms
Side 802 1025 103 5.34% 41.55% 53.11%
SVM 1783 48 99 5.13% 92.38% 2.49% 2000 ms
2-stage Adaboost 1860 4 66 3.42% 96.37% 0.21% 150 ms
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