Person Re-identification for Real-World Scenarios using Deep Learning

Abstract

With the increasing need for efficient monitoring and tracking systems in surveillance, Person Re-identification (Re-ID) has emerged as a pivotal solution for monitoring public spaces and automatically tracking suspicious individuals. While significant advancements have been made in the person Re-ID domain, the challenge of continuously evolving new data remains a critical concern for practical model deployment. Most existing works rely on offline learning, which poses difficulties in adapting to dynamic environments, accommodating evolving identities, and ensuring efficient resource utilization in real world scenarios. To address these limitations, there is a pressing need for an active learning system capable of being incrementally updated with new data. In this research, titled "Person Re-identification for Real-World Scenarios using Deep Learning," we propose an innovative framework for iterative model training on progressively evolving data. We selected two benchmark datasets, Market-1501 and DukeMTMC-reID, and divided them into manageable chunks. Our approach involved training a deep learning model incrementally on these chunks. Following each training session, we updated a Representative Memory with the most characteristic images of each identity from each chunk. These images, stored in the Representative Memory, were then utilized in subsequent model updates, where they were combined with new data for further training. To evaluate the efficacy of our model, we conducted a cross-domain evaluation after each iterative training phase. The results were promising with the model achieving accuracy rates of 77.2% and 68.9% in terms of Rank-5 metrics on the Market-1501 and DukeMTMC-reID test sets, respectively. This research not only demonstrates the feasibility of incremental learning in person Re-ID but also paves the way for future advancements in applying deep learning techniques to real-world surveillance and security applications.