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Related Experiment Video

Updated: May 7, 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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Violent Video Recognition by Using Sequential Image Collage.

Yueh-Shen Tu¹, Yu-Shian Shen², Yuk Yii Chan²

¹Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This study introduces a new method for identifying violent activities using sequential image collages (SIC) with MLP-Mixer models. This approach effectively recognizes violent behavior using less data and computational power than Transformer models.

Keywords:

Transformers behavioral sciences computer architecture image recognition multilayer perceptrons neurons training

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Area of Science:

Computer Science
Artificial Intelligence
Behavior Recognition

Background:

Transformer models excel at behavior recognition but require extensive data.
Existing violent behavior datasets are insufficient for Transformer training.
Transformers can be computationally intensive and may miss temporal dynamics.

Purpose of the Study:

To develop an efficient method for violent behavior recognition using limited data.
To address the computational and data limitations of Transformer models in behavior analysis.
To improve the recognition of temporal features in violent activities.

Main Methods:

Proposed a novel dataset format: sequential image collage (SIC).
Utilized the MLP-Mixer architecture for behavior recognition.
Trained the model on diverse public datasets including hockey fights, CCTV violence, and real-life situations.

Main Results:

The MLP-Mixer model trained with SIC achieved high performance in violent behavior recognition.
The proposed SIC method requires fewer parameters and less computational power (FLOPs) compared to state-of-the-art models.
Demonstrated effective understanding of temporal features in violent actions.

Conclusions:

The sequential image collage (SIC) dataset combined with MLP-Mixer offers an efficient solution for violent behavior recognition.
This approach overcomes the data scarcity and computational challenges associated with Transformer models.
The method shows promise for real-world safety applications requiring robust behavior analysis.