An Adaptive Classifier Based Approach for Crowd Anomaly Detection

作     者：Sofia Nishath P.S.Nithya Darisini 

作者机构：School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：349-364页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Abnormal event detection adaptive GoogleNet neural network classifier multiple instance learning multi-objective whale optimization algorithm 

摘      要：Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and *** video surveillance systems make extensive use of data mining,machine learning and deep learning *** this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep *** this approach,Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded *** use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking *** technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video *** use the multi-objective whale optimization algorithm to optimize the entire process and get the best *** performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the Python simulation *** simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.