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Related Experiment Video

Updated: Jun 17, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Query-Based Object Visual Tracking with Parallel Sequence Generation.

Chang Liu¹, Bin Zhang¹, Chunjuan Bo²

¹School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This summary is machine-generated.

Query decoders improve object tracking by predicting target coordinates in parallel. This novel approach, QPSTrack, enhances speed and performance for accurate object tracking.

Keywords:

object tracking transformer visual tracking

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Query decoders excel in object detection but struggle with object tracking.
Existing sequence-to-sequence methods describe targets as discrete tokens.

Purpose of the Study:

To develop a parallel query-based tracking framework for efficient object tracking.
To improve the performance and speed of query decoder-based tracking.

Main Methods:

Proposed QPSTrack framework using parallel prediction of target coordinate sequences.
Employed a set of queries jointly representing the target, deviating from one-to-one matching.
Utilized an adaptive decoding scheme with a one-layer adaptive decoder and learnable inputs.
Experimented with ViT-Base, ViT-Large, and LeViT architectures as encoder backbones.

Main Results:

QPSTrack achieves a good balance between tracking speed and performance.
The QPSTrack-B256 variant with ViT-Base encoder reached 69.1% AUC on LaSOT benchmark.
Achieved a high speed of 104.8 FPS with the ViT-Base encoder.

Conclusions:

Parallel prediction with query decoders is effective for object tracking.
QPSTrack offers an efficient and accurate solution for real-time object tracking applications.
The framework demonstrates versatility with different encoder architectures.