Online Multi-Object Tracking
25 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
Benchmarks
These leaderboards are used to track progress in Online Multi-Object Tracking
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Libraries
Use these libraries to find Online Multi-Object Tracking models and implementationsDatasets
MOT17
MOTChallenge
MOT16
MOT15
MMPTRACK
BEE23
Oxford Town Center
PersonPath22
CholecTrack20
Latest papers
Track to Detect and Segment: An Online Multi-Object Tracker
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
This new paradigm enables the network to leverage the "context" information of the geometry of objects and allows us to model the interactions among the features of multiple objects.
Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion
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.
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
detectRecog/PointTrack
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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
In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.
A Unified Object Motion and Affinity Model for Online Multi-Object Tracking
yinjunbo/UMA-MOT
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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.
Online Multi-Object Tracking Framework with the GMPHD Filter and Occlusion Group Management
In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection.
FANTrack: 3D Multi-Object Tracking with Feature Association Network
Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN.
Online Multi-Object Tracking with Dual Matching Attention Networks
jizhu1023/DMAN_MOT
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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.
Joint Monocular 3D Vehicle Detection and Tracking
ucbdrive/3d-vehicle-tracking
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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.
