Treffer: Optimized interpretable generalized additive neural networks based malicious activity detection with video surveillance.

Video surveillance continues to have difficulties with identifying the anomalies such as illegal activities and crimes despite the development of interactive multimedia anomaly detection systems. To address this issue, an Optimized Interpretable Generalized Additive Neural Networks based Malicious Activity Detection with Video Surveillance (IGANN-MAD-VS-EOSSOA) is proposed in this paper. Initially, the input videos are collected from UCF-Crime and ShanghaiTech dataset. The collected video is fed to pre-processing for improving the quality of video, removing the noise and enhancing the clarity of image using Multiple Local Particle Filtering (MLPF). The pre-processed video is fed to the segmentation process. Here, the input videos are segmented into image using Maximum Entropy Scaled Super-pixels Segmentation (MESPS). Then the feature extraction is done by Synchro-Transient-Extracting Transform (STET) to extract the features, like color, texture, size, shape, and orientation. The extracted features are provided to the Interpretable Generalized Additive Neural Networks (IGANN) for classifying malicious activity, like Normal, Assault, Fighting, Shooting, Vandalism, Abuse and Accident. In general, IGANN does not adapt any optimization techniques for determining the optimal parameters to assure appropriate categorization. Hence, Elite opposite Sparrow Search Optimization Algorithm (EOSSOA) is proposed to enhance the weight parameter of IGANN for the detection of malicious activity with video surveillance. The proposed IGANN-MAD-VS-EOSSOA method is implemented in Python. The proposed technique attains 26.36%, 20.69% and 30.29% higher accuracy, 19.12%, 28.32%, and 27.84% higher precision when compared with the existing methods: Video anomaly detection scheme with deep convolutional and recurrent techniques (AD-CNN-VS), Toward trustworthy human suspicious activity detection from surveillance videos with deep learning (HSAD-SV-RNN), Deep learning-based real-world object detection and improved anomaly detection for surveillance videos (ROD-DNN-ADSV) respectively. [ABSTRACT FROM AUTHOR]

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