Human Detection in Intelligent Video Surveillance: A Review
Li Hou*,**, Qi Liu*,**, Zhenhai Chen*, and Jun Xu***
*School of Information Engineering, Huangshan University
39 Xihai Road, Tunxi District, Huangshan 245041, China
**School of Communication and Information Engineering, Shanghai University
99 Shangda Road, Baoshan District, Shanghai 200444, China
***Department of Engineering, Daqing Architectural Installation Group Co., Ltd.
3-1 Fengyang Road, Longfeng District, Daqing 163711, China
With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.
-  P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.34, No.4, pp. 743-761, 2012.
-  M. Paul, S. M. E. Haque, and S. Chakraborty, “Human detection in surveillance videos and its applications-a review,” EURASIP J. on Advances in Signal Processing, Vol.2013, No.1, Article 176, 2013.
-  Y. Yasuoka, Y. Shinomiya, and Y. Hoshino, “Simulation of Human Detection System Using BRIEF and Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1159-1164, 2016.
-  R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detecting moving objects, ghosts, and shadows in video streams,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.25, No.10, pp. 1337-1342, 2003.
-  M. Seki, H. Fujiwara, and K. Sumi, “A robust background subtraction method for changing background,” IEEE Workshop on Applications of Computer Vision, pp. 207-213, 2000.
-  S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, “Tracking groups of people,” Computer Vision and Image Understanding, Vol.80, No.1, pp. 42-56, 2000.
-  C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” Proc. 1999 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 246-252, 1999.
-  H. Permuter, J. Francos, and I. Jermyn, “A study of Gaussian mixture models of color and texture features for image classification and segmentation,” Pattern Recognition, Vol.39, No.4, pp. 695-706, 2006.
-  S. Yoon, C. S. Won, K. Pyun, and R. M. Gray, “Image classification using GMM with context information and with a solution of singular covariance problem,” IEEE Proc. of Data Compression Conf., 2003.
-  B. D. Zarit, B. J. Super, and F. K. H. Quek, “Comparison of five color models in skin pixel classification,” Proc. Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58-63, 1999.
-  J. Y. Lee and S. I. Yoo, “An elliptical boundary model for skin color detection,” Int. Conf. Imaging Science, Systems and Technology, 2002.
-  D. Li, L. Xu, E. D. Goodman, Y. Xu, and Y. Wu, “Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking,” Integrated Computer-Aided Engineering, Vol.20, No.3, pp. 201-216, 2013.
-  G. Gualdi, A. Prati, and R. Cucchiara, “Multi-stage sampling with boosting cascades for pedestrian detection in images and videos,” European Conf. on Computer Vision, Computer Vision – ECCV 2010, pp. 196-209, 2010.
-  L. Guo, P.-S. Ge, M.-H. Zhang, L.-H. Li, and Y.-B. Zhao, “Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine,” Expert Systems with Applications, Vol.39, No.4, pp. 4274-4286, 2012.
-  N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
-  J. Wu, N. Liu, C. Geyer, and J. M. Rehg, “C4: A real-time object detection framework,” IEEE Trans. on Image Processing, Vol.22, No.10, pp. 4096-4106, 2013.
-  D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, Vol.60, No.2, pp. 91-110, 2004.
-  A. Satpathy, X. Jiang, and H.-L. Eng, “Human detection by quadratic classification on subspace of extended histogram of gradients,” IEEE Trans. on Image Processing, Vol.23, No.1, pp. 287-297, 2014.
-  Y. Tian, P. Luo, X. Wang, and X. Tang, “Pedestrian detection aided by deep learning semantic tasks,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 5079-5087, 2015.
-  J. Cao, Y. Pang, and X. Li, “Learning multilayer channel features for pedestrian detection,” IEEE Trans. on Image Processing, Vol.26, No.7, pp. 3210-3220, 2017.
-  X. Du, M. El-Khamy, J. Lee, and L. Davis, “Fused DNN: deep neural network fusion approach to fast and robust pedestrian detection,” IEEE Winter Conf. on Applications of Computer Vision (WACV), pp. 953-961, 2017.
-  J. Li, X. Liang, S. Shen, T. Xu, J. Feng, and S. Yan, “Scale-aware fast R-CNN for pedestrian detection,” IEEE Trans. on Multimedia, Vol.20, No.4, pp. 985-996, 2017.
-  A. Mohan, C. Papageorgiou, and T. Poggio, “Example-based object detection in images by components,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.23, No.4, pp. 349-361, 2001.
-  B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. of Computer Vision, Vol.77, No.1-3, pp. 259-289, 2008.
-  P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.32, No.9, pp. 1627-1645, 2010.
-  S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, No.4, pp. 509-522, 2002.
-  W. Ouyang, X. Zeng, and X. Wang, “Learning mutual visibility relationship for pedestrian detection with a deep model,” Int. J. of Computer Vision, Vol.120, No.1, pp. 14-27, 2016.
-  W. Ouyang, X. Zeng, and X. Wang, “Partial occlusion handling in pedestrian detection with a deep model,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.26, No.11, pp. 2123-2137, 2016.
-  S. Zhang, C. Bauckhage, and A. B. Cremers, “Informed haar-like features improve pedestrian detection,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 947-954, 2014.
-  S. Zhang, C. Bauckhage, and A. B. Cremers, “Efficient pedestrian detection via rectangular features based on a statistical shape model,” IEEE Trans. on Intelligent Transportation Systems, Vol.16, No.2, pp. 763-775, 2015.
-  F. Yan, A. M. Iliyasu, A. R. Khan, and H. Yang, “Measurements-based moving target detection in quantum video,” Int. J. of Theoretical Physics, Vol.55, No.4, pp. 2162-2173, 2016.
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