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Person detector for different overhead views using machine learning

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Abstract

We explore a dimension of detecting people with a completely different perspective i.e. use of a top view. An overhead view is often preferred in the cluttered environments because looking down from a top view can afford better coverage and much visibility of a scene. However human detection in such or any other such type of extreme view can be challenging. The reason is that depending on the positions of people in the picture or image, there can be a significant variations in the poses and appearances of a person. To handle all such variety of poses, appearances and body articulations from the perspective of a top view, we propose a novel technique which transforms the region of interest containing a human to standardized the shape. After that applying Rotated Histogram of Oriented Gradient (RHOG) algorithm with machine learning based SVM classifier improves detection performance significantly. We show the potential of our proposed RHOG algorithm across different scenes. When a classifier trained on SCOVIS dataset and applied to our newly recorded overhead datasets named SOTON and IMS, respectively. We achieve a detection rate of 96% and 94%, respectively.

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References

  1. Geronimo D, Lopez AM, Sappa AD, Graf T (2010) Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans Pattern Anal Mach Intell 32(7):1239–1258

    Article  Google Scholar 

  2. Ahmed I, Ahmad A, Piccialli F, Sangaiah AK, Jeon G (2018) A robust features-based person tracker for overhead views in industrial environment. IEEE Internet Things J 5(3):1598–1605

    Article  Google Scholar 

  3. Arbab-Zavar B, Carter JN, Nixon MS (2014) On hierarchical modelling of motion for workflow analysis from overhead view. Mach Vis Appl 25(2):345–359

    Article  Google Scholar 

  4. Cristani M, Raghavendra R, Del Bue A, Murino V (2013) Human behavior analysis in video surveillance: a social signal processing perspective. Neurocomputing 100:86–97

    Article  Google Scholar 

  5. Tang J, Luo J, Tjahjadi T, Guo F (2017) Robust arbitrary-view gait recognition based on 3d partial similarity matching. IEEE Trans Image Process 26(1):7–22

    Article  MathSciNet  Google Scholar 

  6. Al-Zaydi ZQ, Ndzi DL, Kamarudin ML, Zakaria A, Shakaff AY (2017) A robust multimedia surveillance system for people counting. Multimedia Tools Appl 76(22):23777–23804

    Article  Google Scholar 

  7. Aguilar WG, Luna MA, Moya JF, Abad V, Parra H, Ruiz H (2017) Pedestrian detection for uavs using cascade classifiers with meanshift. In: Semantic computing (ICSC), 2017 IEEE 11th International Conference on IEEE, pp 509–514

  8. Nguyen DT, Li W, Ogunbona PO (2016) Human detection from images and videos: a survey. Pattern Recogn 51:148–175

    Article  Google Scholar 

  9. Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vis 38(1):15–33

    Article  Google Scholar 

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: International conference on computer vision and pattern recognition (CVPR’05), vol. 1. IEEE Computer Society, pp 886–893

  11. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Tran Pattern Anal Mach Intell 34(4):743–761

    Article  Google Scholar 

  12. Ahmed I, Adnan A (2018) A robust algorithm for detecting people in overhead views. Cluster Comput 21(1):633–654. https://doi.org/10.1007/s10586-017-0968-3

    Article  Google Scholar 

  13. Cohen I, Garg A, Huang TS (2000) Vision-based overhead view person recognition. In: Proceedings 15th international conference on pattern recognition. ICPR-2000, vol. 1. IEEE, pp 1119–1124

  14. Aradhye H, Fischler M, Bolles R, Myers G (2005) Headprint–person reacquisition using visual features of hair in overhead surveillance video. In: International conference on audio-and video-based biometric person authentication. Springer, pp 879–890

  15. Snidaro L, Micheloni C, Chiavedale C (2005) Video security for ambient intelligence. IEEE Trans Syst Man Cybern-Part A: Syst Humans 35(1):133–144

    Article  Google Scholar 

  16. Yahiaoui T, Meurie C, Khoudour L, Cabestaing F (2008) A people counting system based on dense and close stereovision. In: International conference on image and signal processing. Springer, pp 59–66

  17. Van Oosterhout T, Bakkes S, Kröse BJ et al (2011) Head detection in stereo data for people counting and segmentation. In: VISAPP, pp 620–625

  18. Pang Y, Yuan Y, Li X, Pan J (2011) Efficient hog human detection. Signal Process 91(4):773–781

    Article  Google Scholar 

  19. Nakatani R, Kouno D, Shimada K, Endo T (2012) A person identification method using a top-view head image from an overhead camera. JACIII 16(6):696–703

    Article  Google Scholar 

  20. Del Pizzo L, Foggia P, Greco A, Percannella G, Vento M (2015) A versatile and effective method for counting people on either RGB or depth overhead cameras. In: 2015 IEEE international conference on multimedia and expo workshops (ICMEW). IEEE, pp 1–6

  21. Mukherjee S, Saha B, Jamal I, Leclerc R, Ray N (2011) Anovel framework for automatic passenger counting. In: Image Processing (ICIP), 2011 18th IEEE International Conference on IEEE, pp 2969–2972

  22. Choi T-W, Kim D-H, Kim K-H (2016) Human detection in top-view depth image. Contemp Eng Sci 9(11):547–552

    Article  Google Scholar 

  23. Doulamis A, Kosmopoulos D, Sardis M, Varvarigou T (2008) An architecture for a self configurable video supervision. In: Proceeding of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams. ACM, pp 97–104

  24. Wikipedia (2019) Linear interpolation. https://en.wikipedia.org/wiki/Linear_interpolation, [Online; Accessed 27 Apr 2019]

  25. Chang C, Lin C (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

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Acknowledgements

We are thankful to Institute of Management Sciences and Higher Education Commission (HEC), Pakistan for Grant of PhD scholarship to the main author and funding for NRPU project under Project number 5840/KPK/NRPU/RND/HEC/2016 to support this research work.

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Authors and Affiliations

  1. Centre of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan

    Imran Ahmed, Misbah Ahmad & Awais Adnan

  2. Department of Computer Science, Bahria University, Islamabad, Pakistan

    Awais Ahmad

  3. Department of Computer Science, Sarhad University of Science and Information Technology, Peshawar, Pakistan

    Murad Khan

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  1. Imran Ahmed
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  2. Misbah Ahmad
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  3. Awais Adnan
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  4. Awais Ahmad
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Correspondence to Awais Ahmad.

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Ahmed, I., Ahmad, M., Adnan, A. et al. Person detector for different overhead views using machine learning. Int. J. Mach. Learn. & Cyber. 10, 2657–2668 (2019). https://doi.org/10.1007/s13042-019-00950-5

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