Computer Vision for Human Activity Recognition in Crime Detection

The rapid advancement of computer vision and human activity recognition (HAR) techniques has opened new avenues for enhancing crime detection and prevention. This paper presents an overview of the state-of-the-art methods for using computer vision, with a focus on their application in identifying suspicious or criminal activities. We discuss traditional approaches alongside modern deep learning-based techniques, highlighting their strengths and limitations in real-world scenarios. The challenges of detecting complex activities in diverse environments, low-resolution footage, and varying behavior patterns are addressed. Additionally, potential improvements were made to explore the use of advanced architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for more accurate and efficient crime detection systems. Experimental results on benchmark datasets demonstrate this of these approaches, providing insights into the future development of intelligent surveillance systems.