FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
TLDR
A simple approach which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features allows \emph{FairMOT} to obtain high levels of detection and tracking accuracy and outperform previous state-of-the-arts by a large margin on several public datasets.Abstract:
There has been remarkable progress on object detection and re-identification (re-ID) in recent years which are the key components of multi-object tracking. However, little attention has been focused on jointly accomplishing the two tasks in a single network. Our study shows that the previous attempts ended up with degraded accuracy mainly because the re-ID task is not fairly learned which causes many identity switches. The unfairness lies in two-fold: (1) they treat re-ID as a secondary task whose accuracy heavily depends on the primary detection task. So training is largely biased to the detection task but ignores the re-ID task; (2) they use ROI-Align to extract re-ID features which is directly borrowed from object detection. However, this introduces a lot of ambiguity in characterizing objects because many sampling points may belong to disturbing instances or background. To solve the problems, we present a simple approach \emph{FairMOT} which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The achieved fairness between the tasks allows \emph{FairMOT} to obtain high levels of detection and tracking accuracy and outperform previous state-of-the-arts by a large margin on several public datasets. The source code and pre-trained models are released at this https URL.read more
Citations
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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
TL;DR: YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100.
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Track to Detect and Segment: An Online Multi-Object Tracker
TL;DR: Wu et al. as discussed by the authors proposed TraDeS (TRAck to DEtect and Segment), which infers object tracking offset by a cost volume, which is used to propagate previous object features for improving object detection and segmentation.
Proceedings ArticleDOI
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
TL;DR: Zhang et al. as discussed by the authors proposed a new instance of joint MOT approach based on Graph Neural Networks (GNNs), which can model relations between variablesized objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association.
Posted Content
Quasi-Dense Similarity Learning for Multiple Object Tracking.
TL;DR: Quasi-Dense Similarity Learning is presented, which densely samples hundreds of region proposals on a pair of images for contrastive learning and which outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks.
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