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http://dx.doi.org/10.9717/kmms.2016.19.8.1345

Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance  

Nguyen, Thanh Binh (Dept. of Information and Telecommunication Engineering, Soongsil University)
Nguyen, Van Tuan (Dept. of Information and Telecommunication Engineering, Soongsil University)
Chung, Sun-Tae (Dept. of Smart Systems Software, Soongsil Uniersity)
Cho, Seongwon (School of Electronic and Electrical Engineering, Hongik University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.8, 2016 , pp. 1345-1360 More about this Journal
Abstract
In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.
Keywords
Human Detection; Omni-Directional Camera; CNN (Convolutional Neural Networks); AGMM (Adaptive Gaussian Mixture Model); Visual Surveillance;
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1 N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
2 P.F. Felzenszwalb, R.B. Girshick, D. Mc Allester, and D. Ramanan, "Object Detection with Discriminatively Trained Part-based Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, Issue 9, pp. 1627-1645, 2009.   DOI
3 P. Dollar, S. Belongie, and P. Perona, "The Fastest Pedestrian Detector in the West," Proceeding of The British Machine Vision Conference, pp. 68.1- 68.11, 2010.
4 P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian Detection: An Evaluation of the State of the Art," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 4, pp. 743-761, 2011.   DOI
5 R. Benenson, M. Mathias, R. Timofte, and L. Van Gool, "Pedestrian Detection at 100 Frames per Second," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2903-2910, 2012.
6 R. Benenson, M. Omran, J. Hosang, and B. Schiele, "Ten Years of Pedestrian Detection, What Have We Learned?," Proceeding of European Conference on Computer Vision, pp. 613-627, 2014.
7 B. Hariharan, C.L. Zitnick, and P. Dollár, "Detecting Objects using Deformation Dictionaries," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1995-2002, 2014.
8 K.M Bhuvanarjun and T.C. Mahalingesh, “Pedestrian Detection in a Video Sequence using HOG and Covaraince Method,” International Journal of Electrical and Electronics Engineers, Vol. 7, Issue 1, pp. 183-190, 2015.
9 T.B. Nguyen, V.T. Nguyen, and S.T. Chung, "A Real-time Pedestrian Detection Based on AGMM and HOG for Embedded Surveillance," Journal of Korea Multimedia Society, Vol. 18, No. 11, pp. 1289-1301, 2015.   DOI
10 I. Cinaroglu and Y. Bastanlar, "A Direct Approach for Object Detection with Catadioptric Omnidirectional Cameras," Journal of Signal, Image and Video Processing, Vol. 10, Issue 2, pp 413-420, 2016.   DOI
11 M. Saito, K. Kitaguchi, G. Kimura, and M. Hashimoto "Human Detection from Fish-eye Image by Bayesian Combination of Probabilistic Appearance Models," Proceeding of 2010 IEEE International Conference on Systems Man and Cybernetics, pp. 243-248, 2010.
12 P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun, "Pedestrian Detection with Unsupervised Multi-stage Feature Learning," Proceeding of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3626-3633, 2013.
13 W. Ouyang and X. Wang, "Joint Deep Learning for Pedestrian Detection," IEEE International Conference on Computer Vision, pp. 2056-2063, 2013.
14 Y. Tian, P. Luo, X. Wang, and X. Tang, "Pedestrian Detection Aided by Deep Learning Semantic Tasks," IEEE Conference on Computer Vision and Pattern Recognition, pp. 5079-5087, 2015.
15 F. Liu, Y. Huang, W. Yang, and C. Sun, "High-level Spatial Modeling in Convolutional Neural Network with Application to Pedestrian Detection," Proceeding of IEEE 28th Canadian Conference on Electrical and Computer Engineering, pp.778-783, 2015.
16 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Region-based Convolutional Networks for Accurate Object Detection and Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, Issue 1, pp.142-158, 2015.   DOI
17 You Only Look Once: Unified, Real-Time Object Detection, http://arxiv.org/abs/1506.02640, Aug. 31th, 2016.
18 R. Girshick, "Fast R-CNN," Proceeding of IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
19 D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, "Scalable Object Detection Using Deep Neural Networks," Proceeding of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2155-2162, 2014.
20 R-cnn minus R, http://arxiv.org/abs/1506.06981, Aug. 31th, 2016.
21 C. Stauffer and C, W.E.L Grimson, "Adaptive Background Mixture Models for Real-Time Tracking," Proceeding of Conference on Computer Vision and Pattern Recognition, pp. 246-252, 1999.
22 Recent Advances in Convolutional Neural Networks, https://arxiv.org/abs/1512.07108, Aug. 31th, 2016.
23 K. Fukushima, "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position," Biological Cybernetics, Vol. 36, Issue 4, pp 193-202, 1980.   DOI
24 B. Boser, L. Cun, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel, "Handwritten Digit Recognition with a Backpropagation Network," Proceeding of Advances in Neural Information Processing Systems, pp. 396-404, 1990.
25 A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," Proceeding of Advances in Neural Information Processing Systems, pp. 1106-1114, 2012.
26 Y. Gong, L. Wang, R. Guo, and S. Lazebnik, "Multi-scale Orderless Pooling of Deep Convolutional Activation Features," Proceeding of European Conference Computer Vision, pp.1-17, 2014.
27 M. So, D.K. Han, S.K. Kang, Y.U. Kim, and S.T. Jung, "Recognition of Fainting Motion from Fish-eye Lens Camera Images," Proceeding of The 23rd International Technical Conference on Circuits/Systems, Computers and Communication, pp. 1205-1208, 2008.
28 H. Asanuma, K. Okamoto, and K. Kawamoto, "Feature Learning Based Human Detection for Omnidirectional Images," Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol. 27, pp. 813-824, 2015.   DOI
29 YOLO Project, http://pjreddie.com/darknet/yolo/, (accessed Aug., 10, 2016).
30 Bomni-DB Homepage, http://www.cmpe.boun.edu.tr/pilab/pilabfiles/databases/bomni/, (accessed Aug., 10, 2016).
31 S.W. Jeng and W.H. Tsai, "Using Pano-mapping Tables for Unwarping of Omni-images into Panoramic and Perspective-view Images," Proceeding of IET Image Processing, pp. 149-155, 2007.   DOI