Abstract
Surveillance systems are mainly used to monitor activity and behaviour for the purpose of managing small human group. In particularly, the depicted approach focused on the small human groups stayed in the same place for a while and characterizing the behaviour of the group. The described approach has wide functions in several areas such as surveillance, group interface and behaviour classification. The video surveillance mainly deals with the recognition and classification of group behaviour with respect to several activities such as Normal speech, kicking, hugging and punching. The processing steps include frame generation, segmentation using Fuzzy C-Mean (FCM) clustering; features are extracted using Local Binary Pattern (LBP) and Hierarchical Centroid (HC). Convolution Neural Network (CNN) is used to classify the features. The proposed model is better than existing methods by achieving 80% accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ciptadi, A., Goodwin, M.S., Rehg, J.M.: Movement pattern histogram for action recognition and retrieval. In: Conference on Computer Vision, pp. 695–710. Springer (2014)
Manfredi, M., Vezzani, R., Calderara, S., Cucchiara, R.: Detection of static groups and crowds gathered in open spaces by texture classification. Pattern Recogn. Lett. 44, 39–48 (2014) (Elsevier)
Stephens, K., Bors, A.G.: Human group activity recognition based on modelling moving regions interdependencies. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2115–2120. IEEE (2016)
Tran, K.N., Gala, A., Kakadiaris, I.A., Shah, S.K.: Activity analysis in crowded environments using social cues for group discovery and human interaction modelling. Pattern Recogn. Lett. 44, 49–57 (2014) (Elsevier)
Yang, Y., Zhang, B., Yang, L., Chen, C., Yang, W.: Action recognition using completed local binary patterns and multiple-class boosting classifier. In: 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 336–340. IEEE (2015)
Liciotti, D., Massi, G., Frontoni, E., Mancini, A., Zingaretti, P.: Human activity analysis for in-home fall risk assessment. In: 2015 IEEE International Conference on Communication Workshop (ICCW), pp. 284–289. IEEE (2015)
Kim, Y., Moon, T.: Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13(1), 8–12 (2016)
Al-Nawashi, M., Al-Hazaimeh, O.M., Saraee, M.: A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments. Neural Comput. Appl. 28(1), 565–572 (2017)
John, V., Mita, S., Liu, Z., Qi, B.: Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks. In: IEEE, IAPR International Conference, pp. 246–249 (2015)
Priya, M.M., Nawaz, D.G.K.: MATLAB Based Feature Extraction and Clustering Images Using K-Nearest Neighbor Algorithm (2016)
Lajevardi, S.M., Hussain, Z.M.: Automatic facial expression recognition: feature extraction and selection. Image Video Process. 6(1), 159–169 (2012) (Springer)
Liu, L., Lao, S., Fieguth, P.W., Guo, Y., Wang, X., Pietikäinen, M.: Median robust extended local binary pattern for texture classification. IEEE Trans. Image Process. 25(3), 1368–1381 (2016)
Milan, A., Schindler, K., Roth, S.: Detection-and trajectory-level exclusion in multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3682–3689 (2013)
Mazzon, R., Poiesi, F., Cavallaro, A.: Detection and tracking of groups in crowd. In: IEEE, International Conference, pp. 202–207 (2013)
Liu, L., Shao, L., Li, X., Lu, K.: Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans. Cybern. 46(1), 158–170 (2016)
Chen, C., Jafari, R., Kehtarnavaz, N.: Action recognition from depth sequences using depth motion maps-based local binary patterns. In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1092–1099. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Seemanthini, K., Manjunath, S.S., Srinivasa, G., Kiran, B., Sowmyasree, P. (2020). A Cognitive Semantic-Based Approach for Human Event Detection in Videos. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_25
Download citation
DOI: https://doi.org/10.1007/978-981-15-0077-0_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0076-3
Online ISBN: 978-981-15-0077-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)