An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

Abstract

In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input of the ADSV, a shots boundary detection technique is used to segment prominent frames. Next, The Lightweight Convolution Neural Network (LWCNN) model receives the segmented frames to extract spatial and temporal information from the intermediate layer. Following that, spatial and temporal features are learned using Long Short-Term Memory (LSTM) cells and Attention Network from a series of frames for each anomalous activity in a sample. To detect motion and action, the LWCNN received chronologically sorted frames. Finally, the anomaly activity in the video is identified using the proposed trained ADSV model. Extensive experiments are conducted on complex and challenging benchmark datasets. In addition, the experimental results have been compared to state-ofthe-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.

Computer Vision-Based Monitoring Framework for Forklift Safety at Construction Site

“…While there isn't a specific study focused on checking the authorized forklift operators for driving, numerous authors have explored the issue of abnormal driver behavior. Much attention has been devoted to the development of methods for detecting anomalies in this context (Amin et al, 2023;Okan & Rigoll, n.d.). Blind spot problem has been addressed by different researcher, (Shete et al, 2021) The implementation of an ultrasonic sensor to detect nearby obstacles in a forklift is an effective safety measure.…”