A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Identifying areas of public interest in gathering places is crucial for security and crowd management.
• Existing methods struggle to efficiently identify potential areas of interest and assess their attentional draw.
• Automated detection of important areas can significantly aid security personnel in monitoring public spaces.

Purpose of the Study:

• To propose an automated, two-stage method for detecting important areas in gathering places.
• To develop a novel multi-input attention network (MAN) for classifying important area candidates.
• To introduce a new dataset for evaluating important area detection in gathering places.

Main Methods:

• A two-stage approach combining image processing for candidate generation and a novel multi-input attention network (MAN) for classification.
• Candidate generation utilizes algorithms like K-means++, image dilation, median filtering, and the Rough Set-based Logic Similarity Algorithm (RLSA).
• The MAN integrates global and local image features using channel and spatial attention modules for robust area classification.

Main Results:

• The proposed two-stage method demonstrates good performance in detecting important areas.
• The multi-input attention network effectively fuses features to determine areas of public attention.
• Experimental validation on a new dataset confirms the method's accuracy.

Conclusions:

• The developed two-stage method with MAN provides an effective solution for automatically detecting important areas in gathering places.
• The attention mechanism within MAN enhances the representation and classification of candidate areas.
• This research contributes a valuable tool and dataset for improving surveillance and crowd analysis in public environments.