How Computer Vision Complements Health Care/Nursing Environment
-Anomaly Detection with Hierarchical Classification of Human Motion-
DOI:
https://doi.org/10.52731/liir.v006.349Keywords:
Anomaly Detection, Classification, Health Care/Nursing, Human MotionAbstract
Aging of the population revealed that people of younger generation are facing to take care of more of old generation. It may not be satisfactory to arrange enough number of nursing staffs even in late-evening in hospitals or nursing and personal care facility, or it is not possible to watch every old people continuously in daytime. Detecting anomaly of human motion such as falling down, while no personal care assistant or nurse is accompanied, is expected to complement the insufficiency or lack of care by such staff. However, simple detection of anomaly of human motion such as falling down or sudden stoppage of the staff should be ignored as ‘false positive’ but adaptive detection of such motion by the persons who need assistance is preferable, in order to improve the operability of a system. This paper presents human anomaly detection based on human posture recognition by means of two-stage classification for the solution of abovementioned issues. Proposed method first classifies a person appeared in video into two classes, one corresponds to caregiver and another corresponds to aged people to be taken care of, and then detects anomaly motion only for the latters.
References
Anala M. R，Malika Makker，Aakanksha Ashok，Ambika Sana, "Anomaly Detection in Surveillance Videos", 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop, 2019, pp. 93-98.
Limin Wang，Yu Qiao，Xiaoou Tang, "Action recognition with trajectory-pooled deep-convolutional descriptors", IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 4305-4314.
Amin Ullah, Khan Muhammad, Killichbek Haydarov, "One-Shot Learning for Surveillance Anomaly Recognition using Siamese 3D CNN", 2020 International Joint Conference on Neural Networks, 2020, 8 pp.
J. Lin, Q. Cai, M. Lin, "Multi-label classification of fundus images with graph convolutional network and self-supervised learning", IEEE Signal Process, Vol.28, 2021, pp.454-458.
Devashree R. Patrikar，Mayur Rajaram Parate, "Anomaly Detection by Predicting Future Frames using Convolutional LSTM in Video Surveillance", 2023 2nd International Confer-ence on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, 2023, 6 pp.
Xianlin Zeng，Yalong Jiang，Wenrui Ding，Honggua, "A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos", IEEE Transactions on Circuits and Systems for Video Technology, Vol. 30, No. 1, 2023, pp. 200-212.
Jian Jia，Houjing Huang，Xiaotang Chen，Kaiqi Huang, "Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting", arXiv:2107.03576v2, 2021, 13pp.
H. Huang, W. Yang, J. Lin, G. Huang, J. Xu. G. Wang, X. Chen, K. Huang, "Improve person re-identification with part awareness learning", IEEE Transactions on Image Processing, Vol. 29, 2020, pp. 7468–7481.
Mingzhe Chen, Xi. Xiao, Bin Zhang, Xinyu Liu, Runiu Lu, "Neural Architecture Searching for Facial Attributes-based Depression Recognition", Proc. International Conference on Pattern Recognition, 2022, pp. 877-884.
Dangwei Li, Xiaotang Chen, Zhang Zhang, Kaiqi Huang, "Pose guided deep model for pedestrian attribute recognition in surveillance scenarios", IEEE International Conference on Multimedia and Expo., 2018, 6pp.
S. Zhang, Y. Wang, "Gait Energy Image-Based Human Attribute Recognition Using Two-Branch Deep Convolutional Neural Network", IEEE Transactions on Biometrics, Be-havior, and Identity Science, Vol 5., No. 1, 2023, pp. 53-63.
Camillo Lugaresi, et al., "MediaPipe: A Framework for Building Perception Pipelines", arXiv:1906.08172v1, 2019, 9pp.
