Asian Journal of Information Technology

Year: 2022
Volume: 21
Issue: 1
Page No. 1 - 5

Real-time Burglar Recognition Based on Human Skeletal Data Using Openpose and Long Short Term Memory Network

Authors : Shadiya Mohammed Raly and Priyantha Kumarawadu

References

Ammar, S.M.R., M.D. Anjum, R. Islam and M.T. Islam, 2019. Using deep learning algorithms to detect violent activities. http://dspace.bracu.ac.bd/xmlui/handle/10361/12270

Ammar, S.M.R., M.D. Anjum, R. Islam and M.T. Islam, 2019. Using deep learning algorithms to detect violent activities. http://dspace.bracu.ac.bd/xmlui/handle/10361/12270

Aubry, S., S. Laraba, J. Tilmanne and T. Dutoit, 2019. Action recognition based on 2D skeletons extracted from RGB videos. Matec. Web. Conf., vol. 277. 10.1051/matecconf/201927702034

Aubry, S., S. Laraba, J. Tilmanne and T. Dutoit, 2019. Action recognition based on 2D skeletons extracted from RGB videos. Matec. Web. Conf., vol. 277. 10.1051/matecconf/201927702034

Blackstone, E.A., S. Hakim and B. Meehan, 2020. Burglary reduction and improved police performance through private alarm response. Int. Rev. Law Econ., 10.1016/j.irle.2020.105930

Blackstone, E.A., S. Hakim and B. Meehan, 2020. Burglary reduction and improved police performance through private alarm response. Int. Rev. Law Econ., 10.1016/j.irle.2020.105930

Cao, Z., G. Hidalgo, T. Simon, S.E. Wei and Y. Sheikh, 2019. OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. https://arxiv.org/abs/1812.08008

Cao, Z., G. Hidalgo, T. Simon, S.E. Wei and Y. Sheikh, 2019. OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. https://arxiv.org/abs/1812.08008

Chen, W., Z. Jiang, H. Guo and X. Ni, 2020. Fall detection based on key points of human-skeleton using openPose. Symmetry, 10.3390/sym12050744

Chen, W., Z. Jiang, H. Guo and X. Ni, 2020. Fall detection based on key points of human-skeleton using openPose. Symmetry, 10.3390/sym12050744

Ding, Y., Q. Yang, H. Yu, H. Wang, X. Chen and H. Pu, 2019. Research on real-time behavior recognition method based on deep learning. Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering. 2019 WCSE 307-311.

Ding, Y., Q. Yang, H. Yu, H. Wang, X. Chen and H. Pu, 2019. Research on real-time behavior recognition method based on deep learning. Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering. 2019 WCSE 307-311.

Hussain, Z., M. Sheng and W. E. Zhang, 2019. Different approaches for human activity recognition: A survey. https://arxiv.org/abs/1906.05074

Hussain, Z., M. Sheng and W. E. Zhang, 2019. Different approaches for human activity recognition: A survey. https://arxiv.org/abs/1906.05074

Landi, F., C.G.M. Snoek and R. Cucchiara, 2019. Anomaly locality in video surveillance. https://arxiv.org/abs/1901.10364

Landi, F., C.G.M. Snoek and R. Cucchiara, 2019. Anomaly locality in video surveillance. https://arxiv.org/abs/1901.10364

Li, C., Q. Zhong, D. Xie and S. Pu, 2018. Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018 Jerome Lang, 786-792.

Li, C., Q. Zhong, D. Xie and S. Pu, 2018. Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018 Jerome Lang, 786-792.

Noori, F.M., B. Wallace, M.Z. Uddin and J. Torresen, 2019. A robust human activity recognition approach using openpose, motion features, and deep recurrent neural network. Lecture Notes Com. Sci., 11482: 299-310.
CrossRef  |  Direct Link  |  

Noori, F.M., B. Wallace, M.Z. Uddin and J. Torresen, 2019. A robust human activity recognition approach using openpose, motion features, and deep recurrent neural network. Lecture Notes Com. Sci., 11482: 299-310.
CrossRef  |  Direct Link  |  

Ranasinghe, S., F.A. Machot and H.C. Mayr, 2016. A review on applications of activity recognition systems with regard to performance and evaluation. Int. J.Distributed Sensor Networks, 10.1177/1550147716665520

Ranasinghe, S., F.A. Machot and H.C. Mayr, 2016. A review on applications of activity recognition systems with regard to performance and evaluation. Int. J.Distributed Sensor Networks, 10.1177/1550147716665520

Riaz, R., S.S. Rizvi, A. Mushtaq, S. Shokat and S.J. Kwon, 2019. Burglar detection using deep learning techniques. J. Eng. Appl. Sci., 14: 2672-2686.
CrossRef  |  Direct Link  |  

Ruether, T., 2022. Streaming protocols: Everything you need to know (Update). https://www.wowza.com/blog/streaming-protocols

Ruether, T., 2022. Streaming protocols: Everything you need to know (Update). https://www.wowza.com/blog/streaming-protocols

Shuai, S., M.S. Kavitha, J. Miyao and T. Kurita, 2019. Action classification based on 2D coordinates obtained by real-time pose estimation. International Workshop on Frontiers of Computer Vision (IW-FCV)At: South Korea. 2019 1-6.

Shuai, S., M.S. Kavitha, J. Miyao and T. Kurita, 2019. Action classification based on 2D coordinates obtained by real-time pose estimation. International Workshop on Frontiers of Computer Vision (IW-FCV)At: South Korea. 2019 1-6.

Sultani, W., C. Chen and M. Shah, 2019. Real-world anomaly detection in surveillance videos. https://arxiv.org/abs/1801.04264

Sultani, W., C. Chen and M. Shah, 2019. Real-world anomaly detection in surveillance videos. https://arxiv.org/abs/1801.04264

Sun, K., Xiao, D. Liu and J. Wang, 2019. Deep high-resolution representation learning for human pose estimation. Conference on Computer Vision and Pattern Recognition. 2019 IEEE 5686-5696.

Sun, K., Xiao, D. Liu and J. Wang, 2019. Deep high-resolution representation learning for human pose estimation. Conference on Computer Vision and Pattern Recognition. 2019 IEEE 5686-5696.

Zhu, W., C. Lan, J. Xing, W. Zeng, Y. Li, L. Shen and X. Xie, 2016. Co-occurrence eature learning for skeleton based action recognition using regularized deep LSTM networks. https://arxiv.org/abs/1603.07772

Zhu, W., C. Lan, J. Xing, W. Zeng, Y. Li, L. Shen and X. Xie, 2016. Co-occurrence eature learning for skeleton based action recognition using regularized deep LSTM networks. https://arxiv.org/abs/1603.07772

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved