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Related Experiment Video

Updated: Jun 16, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Intelligent toy tracking trajectory design based on mobile cloud terminal deployment and depth-first search

Yang Zhang¹, Hu Zhang¹

¹Beijing AIQI Technology Co., LTD., Beijing, China.

Peerj. Computer Science

|August 15, 2024

Summary

This summary is machine-generated.

This study introduces an intelligent toy tracking method using Transformer and depth-first search for enhanced toy experiences. The approach ensures accurate multi-target tracking and optimal machine state maintenance on mobile cloud terminals.

Keywords:

Depth-first search Intelligent toy Tracking trajectory

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Area of Science:

Computer Science
Artificial Intelligence
Robotics

Background:

Intelligent toys are increasingly popular, enhancing public life.
There is a need for improved toy tracking for better user experiences.
Existing tracking methods may lack accuracy or efficiency for complex scenarios.

Purpose of the Study:

To propose an advanced intelligent toy tracking method.
To enhance the toy experience through accurate and efficient tracking.
To enable lightweight deployment on mobile cloud terminals.

Main Methods:

A Transformer-based toy detection model was developed for precise image positioning.
An improved depth-first search algorithm was integrated with Long-Short-Term Memory (LSTM) for multi-target tracking.
A lightweight model deployment strategy for mobile cloud terminals was proposed.

Main Results:

The method achieved a mean Average Precision (mAP) of 0.858, indicating world-leading performance.
The tracking system demonstrated excellent accuracy with a Multiple Object Tracking Accuracy (MOTA) value of 0.916.
The proposed lightweight deployment ensures optimal machine state maintenance.

Conclusions:

The developed intelligent toy tracking method offers high accuracy and efficiency.
The integration of Transformer, LSTM, and depth-first search provides robust multi-target tracking.
The mobile cloud terminal deployment makes the method practical and accessible for intelligent toys.