Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Abstract

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

“…To monitor complex video surveillance, CNNs are often used for tasks such as activity and action recognition, anomaly detection, classification, and object detection [31][32][33][34][35], as well as a wide range of other identification, medical image diagnosis, video summarization, and segmentation applications [36][37][38][39][40]. The CNN architecture comprises three main components: the Convolution Layer (CL), pooling layer, and fully linked layer.…”

Using Blockchain Technology and Sharing Culture to Promote Sustainable Forest Management in Tribal Communities

“…(7) Establishing a blockchain for video surveillance equipment: In a technological environment where digital surveillance systems are ubiquitous and continuously producing large amounts of data, manual surveillance is required to identify human activities in the public realm. Meanwhile, smart surveillance systems that can identify normal and abnormal activities are urgently needed because they allow for the effective monitoring of images sent from cameras that are designed to capture abnormal activities; the implementation of these systems can alleviate the lack of surveillance personnel [42,43]. Furthermore, the inclusion of these systems can enhance the community's control over the number of people entering the mountain and the entry of unauthorized personnel.…”

Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos

“…The experiments concluded that their method achieved higher accuracy as compared to randomly initialized filters. Similarly, Hussain et al [ 16 ] have proposed a lightweight 3D CNN model for anomaly activity recognition and camera prioritization in surveillance environments. The variants of 3D CNNs include two-stream 3D CNN [ 17 ], pseudo-3D CNN [ 18 ], and MiCT-Net [ 19 ].…”

“…However, the existing 3D-CNN models can only process 10 to 16 frames effectively. They cannot recognize lengthy activities due to exponential increase in time complexity caused mainly by the temporal dimension [ 16 ]. To overcome this issue, researchers experimented on hybrid models where the spatial features are extracted from pretrained CNN models and learned the temporal information using variants of Recurrent Neural Networks (RNNs).…”