SOTAVerified

Multi-Object Tracking

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.

Papers

Showing 110 of 671 papers

TitleStatusHype
MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results0
YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive AssociationCode0
Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation0
Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art0
Focusing on Tracks for Online Multi-Object TrackingCode2
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object TrackingCode1
FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment0
Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks0
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking0
Asynchronous Multi-Object Tracking with an Event CameraCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPTrackingMOTA77.7Unverified
2ReMOTMOTA76.9Unverified
3SGTMOTA76.8Unverified
4STGTMOTA76.7Unverified
5FairMOTMOTA74.9Unverified
6UniTrackMOTA74.7Unverified
7OUTrack_fmMOTA74.2Unverified
8LMOTMOTA73.2Unverified
9TraDeSMOTA70.1Unverified
10QDTrackMOTA69.8Unverified