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Related Experiment Video

Updated: May 30, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Road Traffic Anomaly Detection by Human-Attention-Assisted Text-Vision Learning.

Yachuang Chai1, Wushouer Silamu1,2

  • 1School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This study introduces a new method for detecting road traffic anomalies using the CLIP model and unique TADS dataset annotations. The approach enhances surveillance accuracy for traffic accidents and congestion.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Transportation Safety

Background:

  • Increasing vehicle numbers exacerbate traffic accident severity and congestion.
  • Accurate detection of traffic anomalies is vital for safety and traffic flow.
  • Deep learning methods face challenges in detecting anomalies in surveillance due to data imbalance and complex patterns.

Purpose of the Study:

  • To develop a more efficient method for detecting road traffic anomalies from surveillance footage.
  • To leverage text and eye-gaze annotations for improved anomaly representation learning.
  • To enhance the accuracy of traffic anomaly detection in challenging surveillance scenarios.

Main Methods:

  • Annotation of the previously proposed TADS dataset with text and eye-gaze data.
Keywords:
multi-modal learningtraffic anomaly detectiontraffic surveillance videovideo anomaly detection

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  • Development of a detection model utilizing the CLIP (Contrastive Language–Image Pre-training) model.
  • Training the model to learn anomaly representations using unique dataset annotations.

    • Main Results:

    • The proposed model demonstrates superior performance in detecting traffic anomalies from a surveillance perspective.
    • The study validates the utility of text and eye-gaze data for enhancing anomaly detection.
    • Experimental results confirm the effectiveness of the CLIP-based approach for traffic surveillance.

    Conclusions:

    • The novel approach effectively improves road traffic anomaly detection using specialized annotations and the CLIP model.
    • Text and eye-gaze data are valuable resources for advancing surveillance-based traffic analysis.
    • This research offers a promising solution for real-world traffic safety and management challenges.