Multi-Object Tracking
204 papers with code • 19 benchmarks • 37 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
Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones
In the second approach, although DeepSORT only processes a quarter of all frames due to hardware and time limitations, our model with DeepSORT (42. 9%) outperforms FairMOT (71. 4%) in terms of recall.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks.
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Online multi-object tracking is a fundamental problem in time-critical video analysis applications.
MPM: Joint Representation of Motion and Position Map for Cell Tracking
Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association).
Quasi-Dense Similarity Learning for Multiple Object Tracking
Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.
Tracking Pedestrian Heads in Dense Crowd
Moreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes.
EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time.
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT
In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0. 50 of 98%.
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles.