Human Suspicious Activity Recognition(HSAR)is a critical and active research area in computer vision that relies on artificial intelligence reasoning.Significant advances have been made in this field recently due to i...Human Suspicious Activity Recognition(HSAR)is a critical and active research area in computer vision that relies on artificial intelligence reasoning.Significant advances have been made in this field recently due to important applications such as video surveillance.In video surveillance,humans are monitored through video cameras when doing suspicious activities such as kidnapping,fighting,snatching,and a few more.Although numerous techniques have been introduced in the literature for routine human actions(HAR),very few studies are available for HSAR.This study proposes a deep convolutional neural network(CNN)and optimal featuresbased framework for HSAR in video frames.The framework consists of various stages,including preprocessing video frames,fine-tuning deep models(Darknet 19 and Nasnet mobile)using transfer learning,serial-based feature fusion,feature selection via equilibrium feature optimizer,and neural network classifiers for classification.Fine-tuning two models using some hit and trial methods is the first challenge of this work that was later employed for feature extraction.Next,features are fused in a serial approach,and then an improved optimization method is proposed to select the best features.The proposed technique was evaluated on two action datasets,Hybrid-KTH01 and HybridKTH02,and achieved an accuracy of 99.8%and 99.7%,respectively.The proposed method exhibited higher precision compared to existing state-ofthe-art approaches.展开更多
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning (KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Republic of Korea (No.20204010600090).
文摘Human Suspicious Activity Recognition(HSAR)is a critical and active research area in computer vision that relies on artificial intelligence reasoning.Significant advances have been made in this field recently due to important applications such as video surveillance.In video surveillance,humans are monitored through video cameras when doing suspicious activities such as kidnapping,fighting,snatching,and a few more.Although numerous techniques have been introduced in the literature for routine human actions(HAR),very few studies are available for HSAR.This study proposes a deep convolutional neural network(CNN)and optimal featuresbased framework for HSAR in video frames.The framework consists of various stages,including preprocessing video frames,fine-tuning deep models(Darknet 19 and Nasnet mobile)using transfer learning,serial-based feature fusion,feature selection via equilibrium feature optimizer,and neural network classifiers for classification.Fine-tuning two models using some hit and trial methods is the first challenge of this work that was later employed for feature extraction.Next,features are fused in a serial approach,and then an improved optimization method is proposed to select the best features.The proposed technique was evaluated on two action datasets,Hybrid-KTH01 and HybridKTH02,and achieved an accuracy of 99.8%and 99.7%,respectively.The proposed method exhibited higher precision compared to existing state-ofthe-art approaches.
