Optimizing motion detection performance: Harnessing the power of squeeze and excitation modules

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Traditional motion detection algorithms often struggle with accuracy and false positives in complex video environments.
• Enhancing segmentation models is crucial for improving the performance of real-time object detection systems.

Purpose of the Study:

• To introduce an innovative segmentation model extending the U-Net architecture with a Squeeze and Excitation (SE) mechanism.
• To improve the detection of moving objects in video streams by integrating the enhanced model with the ViBe motion detection algorithm.

Main Methods:

• The proposed model integrates a Squeeze and Excitation (SE) mechanism into the U-Net architecture.
• This enhanced segmentation model is combined with the ViBe motion detection algorithm.
• Adaptive techniques are employed to increase model robustness in complex scenarios without manual tuning.

Main Results:

• Significant improvements in detection accuracy were observed.
• A notable reduction in false positive rates was achieved.
• The integrated model demonstrated increased robustness in complex scenarios.

Conclusions:

• The developed segmentation model offers a promising direction for real-time motion detection.
• The approach provides high precision and adaptability for varying environmental conditions.
• Further training can optimize the model for specific application requirements.