Online Multi-Object Tracking

24 papers with code • 5 benchmarks • 9 datasets

The goal of Online Multi-Object Tracking is to estimate the spatio-temporal trajectories of multiple objects in an online video stream (i.e., the video is provided frame-by-frame), which is a fundamental problem for numerous real-time applications, such as video surveillance, autonomous driving, and robot navigation.

Source: A Hybrid Data Association Framework for Robust Online Multi-Object Tracking

Libraries

Use these libraries to find Online Multi-Object Tracking models and implementations
2 papers
18

Most implemented papers

Joint Monocular 3D Vehicle Detection and Tracking

ucbdrive/3d-vehicle-tracking ICCV 2019

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.

Online Multi-Object Tracking with Dual Matching Attention Networks

jizhu1023/DMAN_MOT ECCV 2018

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.

FANTrack: 3D Multi-Object Tracking with Feature Association Network

wise-lab/fantrack 7 May 2019

Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN.

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

yinjunbo/UMA-MOT CVPR 2020

In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure.

Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

detectRecog/PointTrack ECCV 2020

The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5. 4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS).

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

detectRecog/PointTrack 3 Jul 2020

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

Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion

SonginCV/MAF_HDA 31 Aug 2020

One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter.

Track to Detect and Segment: An Online Multi-Object Tracker

JialianW/TraDeS CVPR 2021

Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.

Learning to Track with Object Permanence

TRI-ML/permatrack ICCV 2021

In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning.

Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers

alanzty/mo3tr 27 Mar 2021

Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.