Spectator Head Orientation Detection for Stadium Incident Localization
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Solution Overview
Problem
Existing methods for detecting incidents in seated stadium crowds are ineffective and rely heavily on manual control, lacking an automated process to quickly identify potential threats due to the stationary nature of spectators, which complicates the use of computer vision and AI algorithms.
Innovation Solution
An automated method to estimate head orientations of spectators and detect areas of collective attention by analyzing head orientations, generating a signal for potential incidents, which can alert law enforcement or adjust surveillance cameras to focus on areas of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control methods are used to monitor spectator stands, then the complexity of the monitoring system is reduced, but the response time to incidents increases and reliability decreases
Solution Approach 1:
The patent replaces manual mechanical monitoring with automated computer vision and AI algorithms. The system uses image data processing to detect spectator head orientations and identify areas of collective attention, automatically substituting human operators with computational systems that analyze visual data to detect incidents in real-time.
Solution Approach 2:
The patent changes the monitoring approach from tracking physical movements (applicable to moving crowds) to analyzing head orientation parameters (specific to seated spectators). By estimating head orientations from image data and identifying collective attention patterns, the system adapts the monitoring parameters to match the stationary nature of seated audiences while maintaining automated detection capabilities.
2Loss of time
If automated computer vision algorithms are implemented to detect incidents in seated crowds, then response time improves, but the difficulty of detecting and measuring abnormal situations increases
Solution Approach 1:
The patent introduces head orientation estimation as an intermediary metric to detect incidents. Instead of directly detecting complex abnormal situations, the system first measures simple head orientation angles from image data, then uses collective patterns of these orientations to infer areas of interest or incidents, breaking down the complex detection task into measurable intermediate steps.
Solution Approach 2:
The patent enables the spectator crowd to self-reveal information about potential incidents through their collective attention patterns. When spectators naturally orient their heads toward an incident or area of interest, the system captures this self-generated signal, allowing the crowd itself to provide the detection data without requiring external active probing or complex manual investigation.
3Measurement precision
If head orientation estimation is used to detect areas of collective attention, then measurement precision for incident location improves, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The patent segments the spectator stand into multiple neighborhoods or zones and processes head orientation data separately for each segment. By dividing the large monitoring area into smaller regions and analyzing collective attention patterns locally in each segment, the system achieves precise location identification while managing computational complexity through distributed processing of smaller data subsets.
Data Source
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AI summary
We propose an image data processing method for a space containing spectators, for example in a stadium stand. The processing involves: - estimating (P4), in a common neighborhood of spectators in the image, the respective head orientations of spectators in said neighborhood, - detecting (P5), at least as a function of said estimated head orientations, whether said heads are oriented towards a zone of the space, in order to generate (P6), if applicable, a signal containing data from said zone as the area of spectator attention in said space.