Person Re-Identification in Criminal Investigations Using Deep Learning and Vision-Based Technique

Person re-identification (Re-ID) is a key difficulty in criminal investigations, as precisely matching individuals across different camera perspectives is critical for locating suspects and solving crimes. This research investigates the use of deep learning algorithms and vision-based techniques to improve person re-identification in surveillance systems. Our solution uses convolutional neural networks (CNNs) for feature extraction and optimization techniques like triplet loss and softmax loss for effective metric learning. These methods serve to refine inter- and intra-class variations, resulting in more accurate matching across a variety of situations such as changes in viewpoint, occlusion, and lighting. Attention mechanisms and data augmentation approaches were used to deal with difficult scenarios, while adaptive learning rate optimization was used to speed up model convergence. To improve performance even more, re-ranking algorithms and temporal consistency restrictions were combined to increase the accuracy of re-identifying persons across many cameras across time. The suggested system was tested on benchmark datasets such as Market-1501 and Duke MTMC- reID, and it outperformed cutting-edge approaches with a mean Average Precision (mAP) of 85.6% and Rank-1 accuracy of 92.3%. The usage of triplet and softmax loss reduced false positives by 15%, while attention methods and data augmentation increased robustness under different settings. Re-ranking and temporal consistency lead to a 4% boost in Rank-1 accuracy in multi-camera setups, which improved performance in congested or complicated scenarios. Adaptive learning rate optimization reduced training time by 25% while maintaining accuracy. Overall, the results show that our deep learning-based Re-ID system is very successful and scalable for real-world applications, providing law enforcement with a powerful tool for tracking and identifying suspects in criminal investigations.