Multi-Object Tracking
204 papers with code • 19 benchmarks • 36 datasets
Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.
Libraries
Use these libraries to find Multi-Object Tracking models and implementationsSubtasks
Most implemented papers
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
Joint detection and embedding (JDE) based methods usually estimate bounding boxes and embedding features of objects with a single network in Multi-Object Tracking (MOT).
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Multi-Object Tracking (MOT) has been notoriously difficult to evaluate.
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers.
BoT-SORT: Robust Associations Multi-Pedestrian Tracking
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object.
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
Extended Isolation Forest
This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points.
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Our method estimates the object states by adopting a Kalman Filter.
Rethinking the competition between detection and ReID in Multi-Object Tracking
However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS
The decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection.
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.