Survey On Past and Current Trends in Applying Deep Learning Models in Estimating Human Behaviour
Keywords:
Computer Vision, Human Detection, Deep Learning Methods, Image Processing, Action Detection, Interaction RecognitionAbstract
In recent times application based on computer vision are widely used in many fields starting from lifesaving medical devices to home entertainment systems. One of the challenging problems in it is human behaviour estimation. This process of human behaviour prediction systems requires multiple technology integration. Hence it become more important to narrate out the past and the current trends used in human behaviour predictions. This article briefs out the major steps used in the detection process, a literature survey on various contributions and techniques used by different researchers in different period of time and the dataset used by them in training and testing the system. All the systems are compared and the pros and cons were analysed in detail and the research gaps were also discussed. The application of human behaviour estimation extends in various platforms and one of it being the monitoring the use of mobile phones, which are becoming a serious issue in recent times.References
Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 886-893). Ieee.
Pishchulin, L., Jain, A., Wojek, C., Andriluka, M., Thormählen, T., & Schiele, B. (2011, June). Learning people detection models from few training samples. In CVPR 2011 (pp. 1473-1480). IEEE.
Lin, Z., & Davis, L. S. (2008, October). A pose-invariant descriptor for human detection and segmentation. In European conference on computer vision (pp. 423-436). Springer, Berlin, Heidelberg.
Leibe, B., Seemann, E., & Schiele, B. (2005, June). Pedestrian detection in crowded scenes. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 878-885). IEEE.
Johnsen, S., & Tews, A. (2009, May). Real-time object tracking and classification using a static camera. In Proceedings of IEEE International Conference on Robotics and Automation, workshop on People Detection and Tracking (p. 25).
Meng, Q., Li, B., & Holstein, H. (2006). Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach. Image and Vision Computing, 24(8), 795-809.
Liu, Y., Chen, X., Yao, H., Cui, X., Liu, C., & Gao, W. (2009). Contour-motion feature (CMF): A space–time approach for robust pedestrian detection. Pattern Recognition Letters, 30(2), 148-156.
Walk, S., Majer, N., Schindler, K., & Schiele, B. (2010, June). New features and insights for pedestrian detection. In 2010 IEEE Computer society conference on computer vision and pattern recognition (pp. 1030-1037). IEEE.
Xia, L., Chen, C. C., & Aggarwal, J. K. (2011, June). Human detection using depth information by kinect. In CVPR 2011 workshops (pp. 15-22). IEEE.
Davis, M., & Sahin, F. (2016, October). HOG feature human detection system. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 002878-002883). IEEE.
Sreedhar Kumar, S., Ahmed, S. T., & NishaBhai, V. B. Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique. International Journal of Recent Technology and Engineering (IJRTE), 8(10).
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017, July). Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3226-3229). IEEE.
Fei-Fei, L., Fergus, R., & Perona, P. (2004, June). Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop (pp. 178-178). IEEE.
Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008, June). A mobile vision system for robust multi-person tracking. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
Bobick, A. F., & Davis, J. W. (2001). The recognition of human movement using temporal templates. IEEE Transactions on pattern analysis and machine intelligence, 23(3), 257-267.
Chen, D. Y., Shih, S. W., & Liao, H. Y. M. (2007, July). Human action recognition using 2-D spatio-temporal templates. In 2007 IEEE International Conference on Multimedia and Expo (pp. 667-670). IEEE.
Ahmed, S., Guptha, N., Fathima, A., & Ashwini, S. (2021). Multi-View Feature Clustering Technique for Detection and Classification of Human Actions.
Li, W., Zhang, Z., & Liu, Z. (2010, June). Action recognition based on a bag of 3d points. In 2010 IEEE computer society conference on computer vision and pattern recognition-workshops (pp. 9-14). IEEE.
Sun, C., Junejo, I., & Foroosh, H. (2011, November). Action recognition using rank-1 approximation of joint self-similarity volume. In 2011 International Conference on Computer Vision (pp. 1007-1012). IEEE.
Zhang, Z., Tan, T., & Huang, K. (2010). An extended grammar system for learning and recognizing complex visual events. IEEE transactions on pattern analysis and machine intelligence, 33(2), 240-255.
Raptis, M., Kokkinos, I., & Soatto, S. (2012, June). Discovering discriminative action parts from mid-level video representations. In 2012 IEEE conference on computer vision and pattern recognition (pp. 1242-1249). IEEE.
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Copyright (c) 2022 Sajeev Ram, C S Shylaja, K.Kalaivani, Ulagapriya
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