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Updated: Nov 17, 2025

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YolTrack: Multitask Learning Based Real-Time Multiobject Tracking and Segmentation for Autonomous Vehicles.

Xuepeng Chang, Huihui Pan, Weichao Sun

IEEE Transactions on Neural Networks and Learning Systems

|February 12, 2021

Summary

This summary is machine-generated.

YolTrack is a novel real-time multitask network for autonomous vehicles, efficiently performing multiobject tracking and instance segmentation (MOTS). It achieves high speeds, making it suitable for critical driving decisions.

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Autonomous vehicles require robust visual perception for navigation.
Multiobject Tracking and Instance Segmentation (MOTS) are crucial for scene understanding and decision-making.
Existing MOTS methods often struggle with real-time performance demands.

Purpose of the Study:

To develop a real-time multitask neural network for MOTS in autonomous driving.
To address the performance and complexity challenges of current MOTS approaches.
To enable efficient and accurate object tracking and segmentation for vehicle control.

Main Methods:

Proposed YolTrack, a one-stage instance segmentation model for real-time MOTS.
Utilized ShuffleNet V2 with Feature Pyramid Network (FPN) as the backbone.
Integrated segmentation masks with feature maps to enhance tracking accuracy.
Employed optimized geometric mean loss for multi-scale task balancing.

Main Results:

Achieved an inference speed of 29.5 frames per second (fps).
Demonstrated a slight trade-off in accuracy and precision for significant speed gains.
Outperformed state-of-the-art MOTS architectures in real-time performance on the KITTI MOTS dataset.
Validated the importance of foreground segmentation for object tracking.

Conclusions:

YolTrack offers a viable real-time solution for MOTS in autonomous vehicles.
The proposed architecture balances speed and accuracy for practical deployment.
This approach is suitable for enhancing the perception capabilities of self-driving cars.