Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT

arXiv:2609.22291 · cs.CV · Submitted 2026-09-14 · Read on arXiv

cs.CV

Submitted: 2026-09-14

Updated: 2026-09-14

Code: https://github.com/linh-gist/VisualMOT

Terminology

• Accuracy — 36× in this paper · explained in 1 episode
• Benchmark — 25× in this paper · explained in 2 episodes
• Transformer — 21× in this paper · explained in 2 episodes
• Robustness — 17× in this paper · explained in 5 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Kalman Filter — 8× in this paper
• Feature Extraction — 7× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• F1 Score — 4× in this paper · explained in 3 episodes
• Unified Framework — 4× in this paper · explained in 5 episodes
• Average Precision — 3× in this paper
• Computational overhead — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Higher Order Tracking Accuracy — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Cumulative Matching Characteristic — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generalized Labeled Multi-Bernoulli — 1× in this paper
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• IEEE Intelligent Vehicles Symposium — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Tracking Effort Measure — 1× in this paper

Sources

• Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art
• Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey
• MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
• CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios
• YOLOX: Exceeding YOLO Series in 2021
• YOLOv11: An Overview of the Key Architectural Enhancements
• MOT16: A Benchmark for Multi-Object Tracking
• CrowdHuman: A Benchmark for Detecting Human in a Crowd
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• BoT-SORT: Robust Associations Multi-Pedestrian Tracking
• Deformable DETR: Deformable Transformers for End-to-End Object Detection
• DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
• The Mean of Multi-Object Trajectories

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