ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding

arXiv:2609.37801 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-10-01

Code: https://github.com/Toshea111/ByteTraX

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Simple Online and Realtime Tracking with a Deep Association Metric
• Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
• Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking
• MOTR: End-to-End Multiple-Object Tracking with Transformer
• Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art
• Multi-modal Visual Tracking: Review and Experimental Comparison
• ByteTrack: Multi-Object Tracking by Associating Every Detection Box
• You Only Look Once: Unified, Real-Time Object Detection
• ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
• On Calibration of Modern Neural Networks
• I Find Your Lack of Uncertainty in Computer Vision Disturbing
• Robust Models are less Over-Confident
• In Pursuit of Many: A Review of Modern Multiple Object Tracking Systems
• GMOT-40: A Benchmark for Generic Multiple Object Tracking
• SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
• TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
• Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking
• DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video
• CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models