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A Study of Activity Recognition and Questionable Observer Detection

by D. M. Anisuzzaman, A. F. M. Saifuddin Saif
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 15
Year of Publication: 2018
Authors: D. M. Anisuzzaman, A. F. M. Saifuddin Saif
10.5120/ijca2018917855

D. M. Anisuzzaman, A. F. M. Saifuddin Saif . A Study of Activity Recognition and Questionable Observer Detection. International Journal of Computer Applications. 182, 15 ( Sep 2018), 35-42. DOI=10.5120/ijca2018917855

@article{ 10.5120/ijca2018917855,
author = { D. M. Anisuzzaman, A. F. M. Saifuddin Saif },
title = { A Study of Activity Recognition and Questionable Observer Detection },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 182 },
number = { 15 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number15/29942-2018917855/ },
doi = { 10.5120/ijca2018917855 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:32.552366+05:30
%A D. M. Anisuzzaman
%A A. F. M. Saifuddin Saif
%T A Study of Activity Recognition and Questionable Observer Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 15
%P 35-42
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Detecting some specific suspicious activities is a core part of questionable observer detection. Activity recognition and questionable observer detection are important fields of study in artificial intelligent system building and computer vision. Questionable observer detection will not only reduce the workload of thousands of workers but also can prevent crimes. This research investigated existing methods and presented a framework to detect a specific activity. This work has shown the tabular study of algorithms, detected actions, datasets used and accuracy for each type of activity recognitions. This research has also proposed a framework to detect a questionable observer from video on basis of a specific action named avoiding eye contact. The algorithms to detect face, eyes and irises are also described here. This research has also proposed that determining the location of iris in consecutive frames can detect if a person is trying to avoid eye contact.

References
  1. Takai, Miwa. "Detection of suspicious activity and estimate of risk from human behavior shot by surveillance camera." In Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, pp. 298-304. IEEE, 2010.
  2. Doewes, Afrizal, Sri Edi Swasono, and Bambang Harjito. "Feature selection on Human Activity Recognition dataset using Minimum Redundancy Maximum Relevance." In Consumer Electronics-Taiwan (ICCE-TW), 2017 IEEE International Conference on, pp. 171-172. IEEE, 2017.
  3. Dhulekar, P. A., S. T. Gandhe, Anjali Shewale, Sayali Sonawane, and Varsha Yelmame. "Motion estimation for human activity surveillance." In Emerging Trends & Innovation in ICT (ICEI), 2017 International Conference on, pp. 82-85. IEEE, 2017.
  4. Hsu, Yu-Liang, Shyan-Lung Lin, Po-Huan Chou, Hung-Che Lai, Hsing-Cheng Chang, and Shih-Chin Yang. "Application of nonparametric weighted feature extraction for an inertial-signal-based human activity recognition system." In Applied System Innovation (ICASI), 2017 International Conference on, pp. 1718-1720. IEEE, 2017.
  5. Xu, Wanru, Zhenjiang Miao, Xiao-Ping Zhang, and Yi Tian. "Learning a hierarchical spatio-temporal model for human activity recognition." In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 1607-1611. IEEE, 2017.
  6. Shakya, Subarna, Suman Sharma, and Abinash Basnet. "Human behavior prediction using facial expression analysis." In Computing, Communication and Automation (ICCCA), 2016 International Conference on, pp. 399-404. IEEE, 2016.
  7. Hassner, Tal, Yossi Itcher, and Orit Kliper-Gross. "Violent flows: Real-time detection of violent crowd behavior." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pp. 1-6. IEEE, 2012.
  8. Karagiannaki, Katerina, Athanasia Panousopoulou, and Panagiotis Tsakalides. "An online feature selection architecture for Human Activity Recognition." In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 2522-2526. IEEE, 2017.
  9. Mehran, Ramin, Alexis Oyama, and Mubarak Shah. "Abnormal crowd behavior detection using social force model." In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 935-942. IEEE, 2009.
  10. Gowda, Shreyank N. "Human activity recognition using combinatorial Deep Belief Networks." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on, pp. 1589-1594. IEEE, 2017.
  11. Boufama, Boubakeur, Pejman Habashi, and Imran Shafiq Ahmad. "Trajectory-based human activity recognition from videos." In Advanced Technologies for Signal and Image Processing (ATSIP), 2017 International Conference on, pp. 1-5. IEEE, 2017.
  12. Uddin, Md Zia, Weria Khaksar, and Jim Torresen. "Human activity recognition using robust spatiotemporal features and convolutional neural network." In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2017 IEEE International Conference on, pp. 144-149. IEEE, 2017.
  13. Yasin, Hashim, and Shoab Ahmad Khan. "Moment invariants based human mistrustful and suspicious motion detection, recognition and classification." In Computer Modeling and Simulation, 2008. UKSIM 2008. Tenth International Conference on, pp. 734-739. IEEE, 2008.
  14. Matsui, Shinya, Nakamasa Inoue, Yuko Akagi, Goshu Nagino, and Koichi Shinoda. "User adaptation of convolutional neural network for human activity recognition." In Signal Processing Conference (EUSIPCO), 2017 25th European, pp. 753-757. IEEE, 2017.
  15. Chen, Zhenghua, Le Zhang, Zhiguang Cao, and Jing Guo. "Distilling the Knowledge from Handcrafted Features for Human Activity Recognition." IEEE Transactions on Industrial Informatics (2018).
  16. Sunkad, Zubin A. "Feature Selection and Hyperparameter Optimization of SVM for Human Activity Recognition." In Soft Computing & Machine Intelligence (ISCMI), 2016 3rd International Conference on, pp. 104-109. IEEE, 2016.
  17. Barr, Jeremiah R., Kevin W. Bowyer, and Patrick J. Flynn. "Detecting questionable observers using face track clustering." In Applications of Computer Vision (WACV), 2011 IEEE Workshop on, pp. 182-189. IEEE, 2011.
  18. Zhao, Kun, Wei Xi, Zhiping Jiang, Zhi Wang, Hongliang Luo, Jizhong Zhao, and Xiaobin Zhang. "Leveraging Topic Model for CSI Based Human Activity Recognition." In Mobile Ad-Hoc and Sensor Networks (MSN), 2016 12th International Conference on, pp. 23-30. IEEE, 2016.
  19. Maglogiannis, Ilias, Demosthenes Vouyioukas, and Chris Aggelopoulos. "Face detection and recognition of natural human emotion using Markov random fields." Personal and Ubiquitous Computing 13, no. 1 (2009): 95-101.
  20. Cheng, Long, Yani Guan, Kecheng Zhu, Yiyang Li, and Ruokun Xu. "Accelerated Sparse Representation for Human Activity Recognition." In Information Reuse and Integration (IRI), 2017 IEEE International Conference on, pp. 245-252. IEEE, 2017.
  21. De Silva, Liyanage C., Tsutomu Miyasato, and Ryohei Nakatsu. "Facial emotion recognition using multi-modal information." In Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on, vol. 1, pp. 397-401. IEEE, 1997.
  22. Li, Kang, Xiaoguang Zhao, Jiang Bian, and Min Tan. "Sequential learning for multimodal 3D human activity recognition with Long-Short Term Memory." In Mechatronics and Automation (ICMA), 2017 IEEE International Conference on, pp. 1556-1561. IEEE, 2017.
  23. Lee, Song-Mi, Heeryon Cho, and Sang Min Yoon. "Statistical noise reduction for robust human activity recognition." In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2017 IEEE International Conference on, pp. 284-288. IEEE, 2017.
  24. Aramvith, Supavadee, Suree Pumrin, Thanarat Chalidabhongse, and Supakorn Siddhichai. "Video processing and analysis for surveillance applications." In Intelligent Signal Processing and Communication Systems, 2009. ISPACS 2009. International Symposium on, pp. 607-610. IEEE, 2009.
  25. Li, Wanqing, Igor Kharitonenko, Serge Lichman, and Chaminda Weerasinghe. "A prototype of autonomous intelligent surveillance cameras." In Video and Signal Based Surveillance, 2006. AVSS'06. IEEE International Conference on, pp. 101-101. IEEE, 2006.
  26. Chen, Wen-Hui, Carlos Andrés Betancourt Baca, and Chih-Hao Tou. "LSTM-RNNs combined with scene information for human activity recognition." In e-Health Networking, Applications and Services (Healthcom), 2017 IEEE 19th International Conference on, pp. 1-6. 2017.
  27. Savvaki, Sofia, Grigorios Tsagkatakis, Athanasia Panousopoulou, and Panagiotis Tsakalides. "Matrix and Tensor Completion on a Human Activity Recognition Framework." IEEE journal of biomedical and health informatics 21, no. 6 (2017): 1554-1561.
  28. Jarraya, Amina, Khedija Arour, Amel Bouzeghoub, and Amel Borgi. "Feature selection based on Choquet integral for human activity recognition." In Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on, pp. 1-6. IEEE, 2017.
  29. Hjelmås, Erik, and Boon Kee Low. "Face detection: A survey." Computer vision and image understanding 83, no. 3 (2001): 236-274.
  30. Kalavdekar Prakash, N. "Face Detection using Neural Network." International Journal of Computer Applications (0975–8887) 1, no. 14 (2010).
  31. Haro, Antonio, Myron Flickner, and Irfan Essa. "Detecting and tracking eyes by using their physiological properties, dynamics, and appearance." In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, vol. 1, pp. 163-168. IEEE, 2000.
Index Terms

Computer Science
Information Sciences

Keywords

Iris detection Suspicious activity detection Activity recognition Questionable observer detection.

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