Sofia Nishath, P. S. Nithya Darisini*
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 349-364, 2022, DOI:10.32604/cmc.2022.023935
- 24 February 2022
Abstract Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal More >
Login
Register
