CAMELTrack is an Online Multi-Object Tracker that learns to associate detections without hand-crafted heuristics. It combines multiple tracking cues through a lightweight, fully trainable module and achieves state-of-the-art performance while staying modular and fast.

Online Multi-Object Tracking has been recently dominated by Tracking-by-Detection (TbD) methods, where recent advances rely on increasingly sophisticated heuristics for tracklet representation, feature fusion, and multi-stage matching. The key strength of TbD lies in its modular design, enabling the integration of specialized off-the-shelf models like motion predictors and re-identification. However, the extensive usage of human-crafted rules for temporal associations makes these methods inherently limited in their ability to capture the complex interplay between various tracking cues. In this work, we introduce CAMEL, a novel association module for Context-Aware Multi-Cue ExpLoitation, that learns resilient association strategies directly from data, breaking free from hand-crafted heuristics while maintaining TbD's valuable modularity.