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 implementationsDatasets
Latest papers with no code
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role.
An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images
Perception that involves multi-object detection and tracking, and trajectory prediction are two major tasks of autonomous driving.
Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation
This fine-grained representation requires high feature resolution and precise semantic information.
Real-time Online Multi-Object Tracking in Compressed Domain
Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance.
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking
To this end, we propose a strategy, namely multi-view trajectory contrastive learning, in which each trajectory is represented as a center vector.
STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking
The feature difference between current candidate detections and historical tracklets makes the object association much harder.
Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation
In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector.
On the detection-to-track association for online multi-object tracking
Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT).
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs.
Multi-object Tracking with a Hierarchical Single-branch Network
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution.