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Multiple Object Tracking

26 papers with code · Computer Vision
Subtask of Video · Object Tracking

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

Benchmarks

Latest papers with code

An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes

15 Oct 2020Guepardow/Visual-features

Commonly used features are color histograms, histograms of oriented gradients, deep features from convolutional neural networks and re-identification (ReID) features.

MULTIPLE OBJECT TRACKING

7
15 Oct 2020

Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking

ECCV 2020 shijieS/DMMN

Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection. This results in deep models that are detector biased and evaluations that are detector influenced.

MULTIPLE OBJECT TRACKING

35
20 Aug 2020

Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

ECCV 2020 pjl1995/CTracker

Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution.

MULTIPLE OBJECT TRACKING OBJECT DETECTION

160
29 Jul 2020

PointTrack++ for Effective Online Multi-Object Tracking and Segmentation

3 Jul 2020detectRecog/PointTrack

In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.

DATA AUGMENTATION INSTANCE SEGMENTATION MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING SEMANTIC SEGMENTATION

158
03 Jul 2020

Lifted Disjoint Paths with Application in Multiple Object Tracking

ICML 2020 AndreaHor/LifT_Solver

We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors.

MULTIPLE OBJECT TRACKING

34
25 Jun 2020

Quasi-Dense Similarity Learning for Multiple Object Tracking

11 Jun 2020ethvis/qd-track

In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning.

CONTRASTIVE LEARNING METRIC LEARNING MULTIPLE OBJECT TRACKING ONE-SHOT OBJECT DETECTION

38
11 Jun 2020

Learning a Neural Solver for Multiple Object Tracking

CVPR 2020 dvl-tum/mot_neural_solver

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING

153
01 Jun 2020

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

4 Apr 2020ifzhang/FairMOT

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

2,057
04 Apr 2020

Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking

ICLR 2020 anonymousjack/hijacking

Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.

ADVERSARIAL ATTACK AUTONOMOUS DRIVING MULTIPLE OBJECT TRACKING OBJECT DETECTION

26
01 Jan 2020

Learning a Neural Solver for Multiple Object Tracking

16 Dec 2019dvl-tum/mot_neural_solver

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING

153
16 Dec 2019