Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted through person re-identification (ReID) models, play a crucial role by capturing distinctive visual features of the tracked targets. However, despite its importance for tracking, ReID has primarily been studied as an image retrieval problem, with state-of-the-art methods overlooking tracking-specific challenges. This thesis focuses on advancing person re-identification methods, with a particular emphasis on making them more robust and better suited for tracking applications. A key challenge in ReID and MOT is handling occlusions, where targets become partially hidden by objects or other people, leading to degraded re-identification accuracy and potential identity switches in tracking. Additionally, tracking methods often fail to effectively combine ReID with motion cues and scene context, relying instead on naive association strategies. To address these challenges, this thesis makes three key contributions: BPBreID, a part-based method for robust occluded re-identification; KPR, a keypoint promptable ReID model designed to address multi-person occlusions scenarios; and CAMELTrack, an online tracking-by-detection method that replaces traditional heuristic for detection association with a context-aware learnable module. At the time of writing, KPR and CAMELTrack achieve state-of-the-art performance on widely-used benchmarks for occluded person re-identification and multi-object tracking. Complementary to this thesis work, additional research contributions were made in sports analytics, including the development of specialized re-identification models for athletes and the introduction of novel datasets for player tracking, re-identification, and jersey number recognition. The thesis concludes with a critical analysis of the current state of tracking and re-identification technologies, offering my opinionated view about the future of these two rapidly evolving fields.