Multi-object tracking (MOT) has garnered considerable attention due to its relevance in practical applications such as automated devices in smart cities. However, under complex conditions, existing trackers often fail to accurately capture or characterize target motion patterns, exhibiting limitations in flexibility and interpretability. To address these challenges, this paper introduces CHTracker, a confidence-guided hierarchical association paradigm for MOT. By integrating spatial features with varying confidence levels, CHTracker enhances the granularity of motion pattern modeling in edge-case scenarios where conventional trackers are prone to association ambiguity. Our paradigm adaptively utilizes distinct tracking cues and assignment metrics tailored to hierarchical target structures, thereby enabling collaborative tracking. Additionally, CHTracker incorporates the diagonal length of the target bounding box as a state variable during position prediction, which significantly improves the robustness against diverse motion noise. Extensive experimental results on multiple benchmarks, including Dance-Track, MOT17, MOT20, and Singapore Maritime Dataset (SMD), demonstrate that CHTracker achieves the state-of-the-art performance in accuracy, robustness, and generalization. Furthermore, our association paradigm is extended to a visible-infrared fusion version for evaluation on the multimodal CAMEL dataset, underscoring its practical potential to fulfill heterogeneous modality requirements in real-world scenarios. Our code will be available at https://github.com/ZyanChenyang/CHTracker.

Tracking-by-Detection paradigms shine in generic multi-object tracking (MOT), while their compact construction hinders the real-time applications. In this work, we attribute the substantial computational burden to two expensive components, i.e. detection and re-identification. Building upon the principle of adaptively maintaining acceptable inference efficiency, we present Adaptively Sparse Detection with attention-guided refinement (ASDTracker) for efficient tracking. In specific, our ASDTracker rapidly assess the short-term and long-term occlusion, dynamically determining the usage of the expensive detector. For non-key frames, we efficiently refine small-size crops out of Kalman Filter predictions and introduce the noisy shadow labels to robustly train this refinement network. Additionally, we substitute the lightweight appearance representation for the heavy ReID network, which efficiently extracts sufficient appearance cues in the coarsely quantized color spaces. Extensive experiments on four benchmarks demonstrate that ASDTracker achieves competitive performance in generalization and robustness under favorable inference speed. Moreover, the efficient tracking deployment is further implemented to an unmanned surface vehicle with high accuracy and low latency in real-world scenarios.