Vol.71, No.1, 2022, pp.925-939, doi:10.32604/cmc.2022.022147
Identification of Anomalous Behavioral Patterns in Crowd Scenes
  • Muhammad Asif Nauman*, Muhammad Shoaib
Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan
* Corresponding Author: Muhammad Asif Nauman. Email:
Received 28 July 2021; Accepted 03 September 2021; Issue published 03 November 2021
Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade. The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures. Although, researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time. The proposed research work focuses on detection of local and global anomaly detection of crowd. Fusion of spatial-temporal features assist in differentiation of feature trained using Mask R-CNN with Resnet101 as a backbone architecture for feature extraction. The data from, BIWI Walking Pedestrian dataset and the Crowds-By-Examples (CBE) dataset and Self-Generated dataset has been used for experimentation. The data deals with different situations like one set of data deals with normal situations like people walking and acting individually, in a group or in a dense crowd. The other set of data contains images four unique anomalies like fight, accident, explosion and people behaving normally. The simulated results show that in terms of precision and recall, our system performs well with Self-Generated dataset. Moreover, our system uses an early stopping mechanism, which allows our system to outperform to make our model efficient. That is why, on 89th epoch our system starts generating finest results.
Mask R-CNN; crowd management and monitoring; precision and recall
Cite This Article
Nauman, M. A., Shoaib, M. (2022). Identification of Anomalous Behavioral Patterns in Crowd Scenes. CMC-Computers, Materials & Continua, 71(1), 925–939.
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