Person Re-Identification Using Machine Learning

Abstract

Abstract: The goal of this research is to develop a system that can spot real-world anomalies in surveillance footage, like burglaries and assaults. Although different methods for anomaly detection have been explored in prior research, the significance of locality within the frame has not been fully explored. Given the rise in crime rates in many nations, accurate identification of people is essential for deterring and solving crimes. However, storing and processing information about suspects using conventional paper-based systems can be labor-intensive and time- consuming. This illustrates the need for more effective techniques for identifying dead bodies after a crime, which can be done using machine learning and image processing methods. Instead of using whole-frame video segments, this study explores the use of spatiotemporal tubes and presents a novel dataset for crime scene detection with bounding box supervision in both the train and test sets. The experiments show that a network trained with spatiotemporal tubes outperforms a model trained with whole-frame videos, with the locality remaining reliable even when extraction errors occur. Additionally, the network can expand the spatiotemporal crime scene dataset without the need for additional human labelling by producing spatiotemporal proposals for fresh surveillance videos using only video-level labels.