A Person Reidentification Framework for Multi- Camera Tracking by Using Tracking-by-Detection Paradigm

Abstract

The research is based on offline video-based reidentification of individuals by tracking them across multiple non-overlapping cameras, aiming to achieve accurate, efficient, and effective video-based person reidentification by extracting discriminative human attributes and robustly utilizing spatio-temporal complementary features in the tracking process. Person reidentification (Re-ID) aims to identify individuals with the same identities across multiple cameras at particular or different instances of time from images or video sequences. It involves applying deep learning algorithms that help to track people’s positions, motion, and direction. Over the past few years, deep learning- based methods have achieved significant success in this domain. These methods can be categorized as deep metric learning, micro-level feature learning, generative adversarial learning, domain adaptation and transfer learning, sequence feature learning, transformer-based methods, and clustering-based methods. Specifically, sequence feature learning, which focuses on fusion of spatio-temporal information from multiple dimensions, and transfer-learning-based methods, which focus on using pre-trained models trained in one domain and fine-tuning these models by providing samples of the target domain. These methods have achieved better experimental performances compared to the other deep learning-based methods, with significant improvements in accuracy. In the context of person reidentification, persistent challenges such as heavy occlusion and similar appearances, which lead to ID switches, that affect the person reidentification and tracking process, still remain an active area of research. To address these issues, a Single-Camera People Tracking mechanism named Redundancy- Elimination Clustering (REC) is introduced. ID switches mostly occur due to background clutter, heavy occlusion, similar appearances of individuals, or when two or more individuals cross each other in a camera view. These ID switches affect the process of reidentification and tracking. As a person cannot be tracked in different locations at the same time, it becomes clear that the possibility of the occurrence of the ID switches is due to the presence of multiple individuals in the same frame. The frames in which there are multiple individuals are termed here as the Redundant Frames, and we employ Redundancy-Elimination Clustering (REC) to resolve this issue through Hierarchical Clustering. To handle ID switches across multiple cameras, we propose Feature Supervised Clustering (FSC), which utilizes the Hungarian Algorithm and assigns unique global IDs to individuals, enabling robust cross-camera association. To correct the incorrect global IDs assigned to the individuals in the previous Feature Supervised Clustering (FSC) stage, either due to very similar appearances or due to extreme occlusion, we introduce Cross-Camera ID Refinement (CCIR). This method effectively assigns unique global IDs to individuals. In the end, some post-processing, like linear interpolation and handling of edge cases, is also performed. The final proposed method achieved IDF1 (95.67), IDP (96.26), IDR (95.13), Precision (96.26) and Recall (95.86), compared to the base methodology which achieved IDF1 (93.72), IDP (91.8), IDR (95.75), Precision (91.83) and Recall (95.64), which shows the robustness of the proposed method.