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 with no code
A CRF-based Framework for Tracklet Inactivation in Online Multi-Object Tracking
In this paper, a conditional random field (CRF) based framework is put forward to tackle the tracklet inactivation issue in online MOT problems.
Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling
This part of proposed method is based on a proposed similarity metric which is responsible for defining the weight of hypothesized reappeared tracks.
Refinements in Motion and Appearance for Online Multi-Object Tracking
Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association.
Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment
To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models.
Multi-object Tracking with Neural Gating Using Bilinear LSTM
We also propose novel data augmentation approaches to efficiently train recurrent models that score object tracks on both appearance and motion.
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking
In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting.
Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering
In this paper, we propose the methods to handle temporal errors during multi-object tracking.
Heuristic Search for Structural Constraints in Data Association
In this paper, we propose a new heuristic method to search for structural constraints (HSSC) of multiple targets when solving the problem of online multi-object tracking.
Recurrent Autoregressive Networks for Online Multi-Object Tracking
The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory.
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
The visibility map of the target is learned and used for inferring the spatial attention map.
