Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment.
( Image credit: Towards-Realtime-MOT )
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
Ranked #3 on Visual Object Tracking on YouTube-VOS
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 in recent years which are the core components for multi-object tracking.
Ranked #1 on Multi-Object Tracking on MOT20 (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)
During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.
Ranked #9 on Visual Object Tracking on VOT2017/18
Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks.
Ranked #5 on Visual Object Tracking on VOT2017/18
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #5 on Pose Tracking on PoseTrack2017 (using extra training data)
Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in the 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods.
Ranked #2 on 3D Multi-Object Tracking on KITTI