Qdtrack: Quasi-dense Similarity Learning For Appearance-only Multiple Object Tracking

Abstract

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions in images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning. We combine this similarity learning with multiple existing object detectors to build Quasi-Dense Tracking (QDTrack), which does not require displacement regression or motion priors. We find that the resulting distinctive feature space admits a simple nearest neighbor search at inference time for object association. In addition, we show that our similarity learning scheme is not limited to video data, but can learn effective instance similarity even from static input, enabling a competitive tracking performance without training on videos or using tracking supervision. We conduct

Stats

• citations125
• S2 citations—
• github stars0
• HF likes0
• heat score15.75
• arxiv keyfischer2022qdtrack

Related papers

• Quasi-dense Similarity Learning For Multiple Object Tracking (2020)19.58
• Smiletrack: Similarity Learning For Occlusion-aware Multiple Object Tracking (2022)17.36
• Deep Similarity Metric Learning For Real-time Pedestrian Tracking (2018)0.00
• Mmf-track: Multi-modal Multi-level Fusion For 3D Single Object Tracking (2023)7.16
• Search Tracker: Human-derived Object Tracking In-the-wild Through Large-scale Search And Retrieval (2016)5.24
• Few-shot Object Counting With Similarity-aware Feature Enhancement (2022)18.62
• Locality Aware Appearance Metric For Multi-target Multi-camera Tracking (2019)0.00
• DMV: Visual Object Tracking Via Part-level Dense Memory And Voting-based Retrieval (2020)0.00