Efficient anomaly recognition using surveillance videos

Abstract

Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model’s performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources.

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

“…The incorporation of technology and machine learning [4,5] into video surveillance, particularly in 5G and IoT environments, initiates unprecedented possibilities. Automated video surveillance systems controlled by computer vision algorithms [6][7][8] detect anomalies, changes in motion, and intrusions in real-time, reducing reliance on human monitoring [9]. However, challenges persist, such as operator errors, false alarms, and limitations in contextual information within video footage [10][11][12].…”

“…In the context of 5G and IoT, this study addresses technical limitations associated with low-quality videos: specifically, poor lighting and low spatial resolution. These difficulties have an impact on the perceptual quality of video streams [13][14][15] and introduce factors such as poor lighting, camera noise, low spatial resolution, and low frame rates [9,[16][17][18][19]. Despite these challenges, various techniques for detecting anomalies in low-quality surveillance videos have been proposed, [20,21].…”