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 1–10 of 671 papers

TitleStatusHype
MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results—0
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 Evaluation—0
Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art—0
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 Environment—0
Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks—0
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking—0
Asynchronous Multi-Object Tracking with an Event CameraCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReMOTSsMOTSA70.4—Unverified
2UniTracksMOTSA68.9—Unverified
3UnicornsMOTSA65.3—Unverified
4TrackFormersMOTSA54.9—Unverified
5TraDessMOTSA50.8—Unverified
6Track R-CNNsMOTSA40.6—Unverified