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 Light-weight 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-of-the-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.

We present a novel system for anomaly detection in surveillance videos, specifically focusing on identifying instances where individuals deviate from public health guidelines during the pandemic. These anomalies encompassed behaviours like the absence of face masks, incorrect mask usage, coughing, nose-picking, sneezing, spitting, and yawning. Monitoring such anomalies manually was challenging and prone to errors, necessitating automated solutions. To address this, a multi-attention-based deep learning system was employed, utilizing the EfficientNet-B0 architecture. EfficientNet-B0, featuring the Mobile Inverted Bottleneck Convolution (MBConv) block with Squeeze-and-Excitation (SE) modules, emphasizes informative channel characteristics while disregarding irrelevant ones. However, this approach neglected crucial spatial information necessary for visual recognition tasks. To improve this, the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B0 to improve feature extraction. The baseline EfficientNet-B0 model’s SE module was replaced with the CBAM module within each MBConv module to retain spatial information related to anomaly activities. Additionally, the CBAM module, when embedded after the second convolutional layer, was observed to significantly enhance the classification ability of the model across different anomaly classes, resulting in a significant accuracy boost from 87 to 96%. In conclusion, we demonstrated the efficacy of the CBAM module in refining feature extraction and improving the entire classification performance of the proposed method, showcasing its potential for robust anomaly detection in surveillance videos.

Hand Gesture Recognition (HGR) is a promising research area with an extensive range of applications, such as surgery, video game techniques, and sign language translation, where sign language is a complicated structured form of hand gestures. The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers, the orientation of the hand, and the hand’s position concerning the body. The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population. Therefore, real-time HGR is one of the most effective interaction methods between computers and humans. Developing a user-free interface with good recognition performance should be the goal of real-time HGR systems. Nowadays, Convolutional Neural Network (CNN) shows great recognition rates for different image-level classification tasks. It is challenging to train deep CNN networks like VGG-16, VGG-19, Inception-v3, and Efficientnet-B0 from scratch because only some significant labeled image datasets are available for static hand gesture images. However, an efficient and robust hand gesture recognition system of sign language employing finetuned Inception-v3 and Efficientnet-Bo network is proposed to identify hand gestures using a comparative small HGR dataset. Experiments show that Inception-v3 achieved 90% accuracy and 0.93% precision, 0.91% recall, and 0.90% f1-score, respectively, while EfficientNet-B0 achieved 99% accuracy and 0.98%, 0.97%, 0.98%, precision, recall, and f1-score respectively.