Body-part Tubelet Transformer for Human-Related Crime Classification

 2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 

Detecting human-related crimes from surveillance videos poses an increasingly difficult challenge, especially when confronted with human actions that are relatively similar. In this work, we propose a transformer-based model that induces bias through the incorporation of a Tubelet embedder module-a 3D convolutional layer. The aim is to capture spatiotemporal embeddings from skeletal trajectories extracted from videos using 3D convolutional operations. Our experiments are conducted on the Human-Related Crime dataset, revealing that the use of tubelet embeddings maintains competitive performance (49% accuracy) to the state-of-the-art, while considerably reducing the computational complexity of the model. . See more 

An Intelligent System for Complex Violence Pattern Analysis and Detection (International Journal of Intelligent Systems. [IF: 10.312, Rank Q1])(2022)

Video surveillance has shown encouraging outcomes to monitor human activities and prevent crimes in real time. To this extent, violence detection (VD) has received substantial attention from the research community due to its vast applications, such as ensuring security over public areas and industrial settings through smart machine intelligence. However, because of changing illumination, complex background and low resolution, the analysis of violence patterns remains challenging in the industrial video surveillance domain. In this work, we propose a computationally intelligent VD approach to precisely detect violent scenes through deep analysis of surveillance video sequential patterns. See more

AI-Assisted Edge Vision for Violence Detection in IoT-Based Industrial Surveillance Networks (IEEE Transaction on Industrial Informatics, [IF: 11.648, Rank Q1]) (2022)

Analyzing surveillance videos is mandatory for the public and industrial security. Overwhelming growth in computer vision fields has been made to automate the surveillance system in terms of human activity recognition, such as behavior analysis and violence detection (VD). However, it is challenging to detect and analyze the violent scenes intelligently to fulfill the notion of Industrial Internet of Things (IIoT)-based surveillance buoyed by constrained resources to reduce computational power. To tackle this challenge, in this article, an artificial intelligence enabled IIoT-based framework with VD-Network (VD-Net) is proposed. First, the input video frames are passed to light-weight convolutional neural network model for important information collection including humans or suspicious objects such as knives/guns. See more

A Comprehensive Review on Vision-based Violence Detection in Surveillance Videos (ACM Computing Surveys. [IF: 14.324, Rank Q1])(2022)

Recent advancements in intelligent surveillance systems for video analysis have been a topic of great interest in the research community due to the vast number of applications to monitor humans’ activities. The growing demand for these systems aims towards automatic violence detection (VD) systems enhancing and comforting human lives through artificial neural networks (ANN) and machine intelligence. Extremely overcrowded regions such as subways, public streets, banks, and the industries need such automatic VD system to ensure safety. and security in the smart city. For this purpose, researchers have published extensive VD literature in the form of surveys, proposals, and extensive reviews. See more

Raspberry Pi Assisted Facial Expression Recognition Framework for Smart Security in Law-enforcement Services (Information Science, [IF: 5.910, Rank Q1])(2019)

Facial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. See more

Randomly Initialized CNN with Densely Connected Stacked Autoencoder for Efficient Fire Detection (Engineering Applications of Artificial Intelligence, [IF: 7.802, Rank Q1]) (2022)

Vision sensors-based fire detection is an interesting and useful research domain with significant alleviated attention from computer vision experts. The baseline research is based on low-level color features, lately replaced by the effective representation of deep models, achieving better accuracy, but higher false alarm rates still exist with expensive computations. Furthermore, the current feed-forward neural networks initialize and allocate the weights according to the input shape, posing a vanishing gradient problem with slow convergence speed. The main challenges associated with fire detection are the limited performance of the developed models in terms of accuracy, higher false alarm rates. See more

Deep Learning Assists Surveillance Experts: Toward Video Data Prioritization

 (IEEE Transaction on Industrial Informatics, [IF: 11.648, Rank Q1]) (2022)

Video summarization (VS) suppresses high-dimensional (HD) video data by only extracting only the important information. However, prior research has not focused on the need for surveillance VS, that is used for many applications to assist video surveillance experts, including video retrieval and data storage. In addition, mainstream techniques commonly use 2D deep models for VS, ignoring event occurrences. Accordingly, we present a two-fold 3D deep learning-assisted VS framework. First, we employ an inflated 3D ConvNet model to extract temporal features; these features are optimized using a proposed encoder mechanism. The input video is temporally segmented using a feature See more