Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.
Source: SOT for MOT
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Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
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.
Ranked #1 on Multi-Object Tracking on MOT16 (using extra training data)
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Ranked #2 on Multi-Object Tracking on MOT16 (using extra training data)
The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.
Ranked #2 on Multiple Object Tracking on KITTI Tracking test
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Ranked #3 on Multi-Object Tracking on 2D MOT 2015
In this paper, we harness the power of deep learning for data association in tracking by jointly modelling object appearances and their affinities between different frames in an end-to-end fashion.
In this project, we implement a multiple object tracker, following the tracking-by-detection paradigm, as an extension of an existing method.
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.