Augmenting a thermodynamically structured neural network with an environmental MLP predicts cell trajectories and mitotic events from video features at claimed accuracies above 85%.
Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors. Despite this, little work has been done to incorporate appearance cues beyond simple heuristic models that lack robustness to feature degradation. In this paper, we propose a novel way to leverage objects' appearances to adaptively integrate appearance matching into existing high-performance motion-based methods. Building upon the pure motion-based method OC-SORT, we achieve 1st place on MOT20 and 2nd place on MOT17 with 63.9 and 64.9 HOTA, respectively. We also achieve 61.3 HOTA on the challenging DanceTrack benchmark as a new state-of-the-art even compared to more heavily-designed methods. The code and models are available at \url{https://github.com/GerardMaggiolino/Deep-OC-SORT}.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Augmented Structure Preserving Neural Networks for cell biomechanics
Augmenting a thermodynamically structured neural network with an environmental MLP predicts cell trajectories and mitotic events from video features at claimed accuracies above 85%.