UT-Trackformer: Comprehensive Analysis

Overview

This comprehensive analysis demonstrates the superior performance of UT-Trackformer over the baseline Trackformer model. By integrating UniTrack's novel graph-based loss function, we enhance multi-object tracking performance by effectively addressing post-occlusion ID switches, temporal inconsistencies, and cross-subject ID swaps. This improvement showcases how UniTrack can be seamlessly incorporated into existing state-of-the-art MOT architectures, significantly boosting their tracking robustness and identity preservation. Our analysis covers multiple aspects of tracking performance across the MOT17 dataset, including:

About the MOT17 Dataset

The MOT17 dataset is a standard benchmark for multi-object tracking, containing 14 challenging video sequences with various camera angles, lighting conditions, and object densities. Our evaluation uses both training and validation sequences to provide a comprehensive assessment of tracking performance.

The metrics shown below represent normalized scores (0-1 scale) where higher values indicate better performance. Each metric compares UT-Trackformer against the baseline Trackformer model, showing clear improvements across all evaluation criteria.

Performance Metrics

The following metrics show the average performance across all MOT17 sequences. UT-Trackformer consistently outperforms the baseline Trackformer model across all metrics.

Stability Score

UT-Trackformer
0.81
Trackformer
0.67
+21.5% improvement

Occlusion Recovery

UT-Trackformer
0.84
Trackformer
0.67
+24.4% improvement

Drift Correction

UT-Trackformer
0.90
Trackformer
0.73
+23.2% improvement

Complex Scene Handling

UT-Trackformer
0.87
Trackformer
0.70
+23.4% improvement

Temporal Analysis

The following sequences demonstrate the temporal tracking performance of both models. Each comparison shows Trackformer (top) and UT-Trackformer (bottom) tracking the same scene. The metrics for each sequence show the clear superiority of UT-Trackformer.

Static Comparisons

These static comparisons highlight specific scenarios where UT-Trackformer shows significant improvements:

Static Comparison

Comparison Complex Scenes Mot17-02 289-299

Static Comparison

Comparison Complex Scenes Mot17-02 289-299 1

Static Comparison

Comparison Complex Scenes Mot17-05 143-151

Static Comparison

Comparison Complex Scenes Mot17-05 301-309

Static Comparison

Comparison Complex Scenes Mot17-09 369-378

Static Comparison

Comparison Complex Scenes Mot17-09 369-378 3

Static Comparison

Comparison Id Stability Mot17-02 211-219

Static Comparison

Comparison Id Stability Mot17-02 211-219 4

Static Comparison

Comparison Id Stability Mot17-05 152-160

Static Comparison

Comparison Id Stability Mot17-05 152-160 5

Static Comparison

Comparison Id Stability Mot17-09 348-358

Static Comparison

Comparison Id Stability Mot17-09 348-358 7

Static Comparison

Comparison Long Term Tracking Mot17-02 198-208

Static Comparison

Comparison Long Term Tracking Mot17-05 170-180

Static Comparison

Comparison Long Term Tracking Mot17-09 308-316

Static Comparison

Comparison Occlusion Handling Mot17-02 219-226

Static Comparison

Comparison Occlusion Handling Mot17-05 341-350

Static Comparison

Comparison Occlusion Handling Mot17-09 375-381

Static Comparison

Comparison Recovery From Drift Mot17-02 257-263

Static Comparison

Comparison Recovery From Drift Mot17-02 257-263 14

Static Comparison

Comparison Recovery From Drift Mot17-05 333-341

Static Comparison

Comparison Recovery From Drift Mot17-09 338-343