Two-stage classification approach for human detection in camera video in bulk ports

• Autorzy: Mi C. , Zhang Z. , He X. , Huang Y. , Mi W.

Identyfikatory

DOI

10.1515/pomr-2015-0049

• Języki publikacji: EN

Abstrakty

EN

With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG) features of the human body will show great different between front & back standing (F&B) and side standing (Side) human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.

Słowa kluczowe

EN

Human Detection; Histograms of Oriented Gradients; support vector machine; classification

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2015
• Tom: S 1
• Strony: 163--170
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Mi C.

• Container Supply Chain Tech. Engineering Research Center, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

autor

Zhang Z.

• Logistics Engineering College, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

autor

He X.

• Logistics Engineering College, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

autor

Huang Y.

• Container Supply Chain Tech. Engineering Research Center, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

autor

Mi W.

• Container Supply Chain Tech. Engineering Research Center, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

Bibliografia

• 1. Mi, Chao; He, Xin; Liu, Haiwei; Huang, Youfang; Mi, Weijian, “Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports”, Mathematical Problems in Engineering, 2014, DOI: 10.1155/2014/386764
• 2. Mi C, Shen Y, Mi W, et al. “Ship Identification Algorithm Based on 3D Point Cloud for Automated Ship Loaders”. Journal of Coastal Research, 2015, 73(sp1): 28-34, DOI: 10.2112/SI73-006.1
• 3. Bian Zhicheng, Yang Yongsheng, Mi Weijian, Mi Chao. “Dispatching electric AGVs in Automated Container Terminals with long travel distance”. Journal of Coastal Research, 2015, 73(sp1): 75-81, DOI: 10.2112/SI73-014.1
• 4. Xiaoming Yang, Ning Zhao,Zhicheng Bian,Jiaqi Chai, and Chao Mi. “An intelligent storage determining method for inbound containers in container terminals”. Journal of Coastal Research, 2015, 73(sp1): 197-204, DOI: 10.2112/ SI73-035.1
• 5. Yung-Chi Lo; Po-Yen Lee ;Shyi-Chyi Cheng. “Space-time template matching for human action detection using volume-based Generalized Hough transform”. Image Processing (ICIP), 2011 18th IEEE International Conference on. pp.2097 – 2100(2011), DOI: 10.1109/ICIP.2011.6116021
• 6. SongminJia; ShuangWang ;Lijia Wang et al. “Robust human detecting and tracking using varying scale template matching”. Information and Automation (ICIA), 2012 International Conference on. pp. 25 – 30(2012), DOI: 10.1109/ICInfA.2012.6246776
• 7. Navneet Dalal and Bill Triggs, 2005. “Histograms of oriented gradients for human detection”.. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, pp.886- 893(2005), DOI: 10.1109/ CVPR.2005.177
• 8. Hong Tian, Zhu Duan, Ajith Abraham, Hongbo Liu.” A novel multiplex cascade classifier for pedestrian detection”. Pattern Recognition Letters, Volume 34, Issue 14, 15 October 2013, Pages 1687-1693, DOI: 10.1016/j.patrec.2013.04.015
• 9. Hai-Miao Hu, Xiaowei Zhang, Wan Zhang, Bo Li.” Joint global–local information pedestrian detection algorithm for outdoor video surveillance”. Journal of Visual Communication and Image Representation, Volume 26, January 2015, Pages 168-181, DOI: 10.1016/j.jvcir.2014.11.009
• 10. Qiang Zhu; Yeh, M.-C. ; Kwang-Ting Cheng.et al. “Fast Human Detection Using a Cascade of Histograms of Oriented Gradients”. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol.2, pp.1491 – 1498(2006), DOI: 10.1109/CVPR.2006.119
• 11. Cristina Conde, Daniela Moctezuma, et.al.“HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments” Neurocomputing, Volume 100, 16 January 2013, Pages 19-30, DOI: 10.1016/j. neucom.2011.12.037
• 12. Marco Pedersoli, Jordi Gonzàlez, Andrew D. Bagdanov, Xavier Roca. ”Efficient discriminative multiresolution cascade for real-time human detection applications”. Pattern Recognition Letters, Volume 32, Issue 13, 1 October 2011, Pages 1581-1587, DOI: 10.1016/j.patrec.2011.06.019
• 13. Ninomiya, H; Ohki, H.; Gyohten, K. et al. “An evaluation on robustness and brittleness of HOG features of human detection”. Frontiers of Computer Vision (FCV), 2011 17th Korea-Japan Joint Workshop on. pp. 1 – 5( 2011), DOI: 10.1109/FCV.2011.5739746
• 14. Ying Tan; Jun Wang. “A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension”. Knowledge and Data Engineering, IEEE Transactions on. Vol.16, pp 385 – 395(2004), DOI: 10.1109/ TKDE.2004.1269664
• 15. QiuXintao; Fu Dongmei ; Yang Tao. “A novel approach to optimize the objective function based on VC dimension and structural risk minimization”. Control Conference (CCC), 2011 30th Chinese. pp. 3226 – 3230(2011)
• 16. Ando, H. Fujiyoshi, H. “Human-Area Segmentation by Selecting Similar Silhouette Images Based on WeakClassifier Response”. Pattern Recognition (ICPR), 2010 20th International Conference on. pp. 3444 – 3447(2010). DOI: 10.1109/ICPR.2010.841
• 17. Jiayuan Yu. “The application of BP-Adaboost strong classifier to acquire knowledge of student creativity”. Computer Science and Service System (CSSS), 2011 International Conference on. pp.669 – 2672(2011). DOI: 10.1109/CSSS.2011.5974999
• 18. Lie Guo, Ping-Shu Ge, Ming-Heng Zhang, Lin-Hui Li, Yi-Bing Zhao.” Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine” Expert Systems with Applications, Volume 39, Issue 4, March 2012, Pages 4274-4286,DOI: 10.1016/j.eswa.2011.09.106
• 19. Huimin Qian, Yaobin Mao, Wenbo Xiang, Zhiquan Wang.” Recognition of human activities using SVM multiclass classifier”. Pattern Recognition Letters, Volume 31, Issue 2, 15 January 2010, Pages 100-111, DOI: 10.1016/j. patrec.2009.09.019
• 20. Kanitkar, A. Bharti, B. ;Hivarkar, U.N. “Vision based preceding vehicle detection using self shadows and structural edge features”. Image Information Processing (ICIIP), 2011 International Conference on. pp.1 – 6(2011), DOI: 10.1109/ICIIP.2011.6108922
• 21. Man Zhang; Zhenan Sun ;Tieniu Tan. “Deformed iris recognition using bandpass geometric features and lowpass ordinal features”. Biometrics (ICB), 2013 International Conference on. pp.1 – 6(2013), DOI: 10.1109/ ICIIP.2011.6108922
• 22. Yu Cheng; ZhigangJin; CunmingHao. “Illumination normalization based on 2D Gaussian illumination model”. Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on. Vol.3, pp.V3-451 - V3-455(2010). DOI: 10.1109/ICACTE.2010.5579870
• 23. Ying Bai; Dali Wang. “On the Comparison of Trilinear, Cubic Spline, and Fuzzy Interpolation Methods in the High-Accuracy Measurements”. Fuzzy Systems, IEEE Transactions on. Vol.8, pp. 1016 – 1022(2010), DOI: 10.1109/ TFUZZ.2010.2064170
• 24. Corinna Cortes; Vladimir Vapnik. “Support-vector networks”. Machine Learning. Vol.20, Issue 3, pp 273297(1995), DOI: 10.1007/BF00994018

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.