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.
Ranked #10 on Video Instance Segmentation on YouTube-VIS validation
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 #5 on Multi-Object Tracking on MOT16 (using extra training data)
Such approaches are however prone to fail in case of e. g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking.
3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval.
Ranked #1 on Monocular 3D Object Detection on Google Objectron
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