Browse > Computer Vision > Object Tracking > Multi-Object Tracking

Multi-Object Tracking

10 papers with code · Computer Vision
Subtask of Object Tracking

State-of-the-art leaderboards

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Greatest papers with code

A Baseline for 3D Multi-Object Tracking

9 Jul 2019xinshuoweng/AB3DMOT

Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results.

3D MULTI-OBJECT TRACKING AUTONOMOUS DRIVING

Exploit the Connectivity: Multi-Object Tracking with TrackletNet

18 Nov 2018zhengthomastang/2018AICity_TeamUW

Multi-object tracking (MOT) is an important and practical task related to both surveillance systems and moving camera applications, such as autonomous driving and robotic vision.

AUTONOMOUS DRIVING MULTI-OBJECT TRACKING

Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking

26 Feb 2018JunaidCS032/MOTBeyondPixels

This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios.

MULTI-OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING

MOT16: A Benchmark for Multi-Object Tracking

2 Mar 2016yihongXU/deepMOT

Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING MULTIPLE PEOPLE TRACKING

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

CVPR 2019 zhen-he/tracking-by-animation

To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames.

MULTI-OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

CVPR 2019 zhen-he/tracking-by-animation

To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames.

MULTI-OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING

Online Multi-Object Tracking with Dual Matching Attention Networks

ECCV 2018 jizhu1023/DMAN_MOT

In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets.

MULTI-OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING

INFER: INtermediate representations for FuturE pRediction

26 Mar 2019talsperre/INFER

Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving).

ACTIVITY PREDICTION FUTURE PREDICTION MULTI-OBJECT TRACKING TRAJECTORY PREDICTION