Context-Based Crime Detection : A Framework Integrating Computer Vision Technologies

Implementing a Computer Vision approach for crime detection through deep learning and computer vision techniques has become increasingly popular in recent years. This approach allows for visual data analysis, identification of potential illegal activities, and provision of valuable insights to law enforcement agencies. Our approach, however, distinguishes itself from others by incorporating stance information, thereby capturing critical behaviors such as scanning surroundings, pacing, and sudden changes in direction that often precede criminal acts. Object recognition using deep learning poses a significant challenge in computer vision. It aims to identify objects from a vast array of predefined categories within natural images. Given the rapid advancements made in this field, our paper presents a comprehensive examination of region-based convolutional neural networks, specifically Faster R-CNN. The primary application of YOLOv5 in the context of object detection is to extract features from input images and detect criminal activities. We also conduct experimental analyses to compare various methodologies and draw meaningful conclusions. We propose future research directions for object detection systems and neural network-based learning, emphasizing a privacy-conscious approach integrating metadata and ethical considerations. Rigorous testing and continuous refinement enhance system reliability. Our approach leverages contextual awareness, accuracy, and precision to enhance crime detection and foster safer environments. Expertise in integrating deep learning techniques and computer vision methodologies ensures reliability and effectiveness.