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
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
LARGE-SCALE PERSON RE-IDENTIFICATION MULTIPLE OBJECT TRACKING VIDEO INSTANCE SEGMENTATION
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)
FAIRNESS MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING OBJECT DETECTION
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.
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)
MULTIPLE OBJECT TRACKING MULTI-TASK LEARNING REAL-TIME MULTI-OBJECT TRACKING
Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
Ranked #2 on
Multiple Object Tracking
on KITTI Tracking test
MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING OBJECT DETECTION
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
3D MULTI-OBJECT TRACKING AUTONOMOUS DRIVING MULTIPLE OBJECT TRACKING
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 #8 on
Multiple Object Tracking
on KITTI Tracking test
3D OBJECT DETECTION 3D POSE ESTIMATION AUTONOMOUS VEHICLES MULTIPLE OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING TRAJECTORY PREDICTION
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 #4 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.
Query-key mechanism in single-object tracking(SOT), which tracks the object of the current frame by object feature of the previous frame, has great potential to set up a simple joint-detection-and-tracking MOT paradigm.