Zone-Based Foreign Object Detection for Gate Passage Tracking
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Solution Overview
Problem
In gate systems, particularly face authentication ticket gates, it is challenging to accurately determine whether a person has passed through the passageway without using a radio card, due to difficulties in image-based tracking caused by variations in hair color, clothing, lighting, and environmental factors, which can lead to decreased determination accuracy and require time-consuming setting or learning for each capture environment.
Innovation Solution
An information processing apparatus and method that divides the passageway image into zones, using foreign object detection based on deep learning to track a person's movement by comparing images in stationary and non-stationary states, determining passage by analyzing time-series changes in zone detection, thereby improving determination accuracy without the need for constant entrance detection in each zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based tracking is used to determine whether a person has passed through the gate, then passage detection can be achieved, but determination accuracy decreases due to variations in hair color, clothing, lighting, and environmental factors
Solution Approach 1:
The passageway is divided into multiple zones (first zone, second zone, third zone) along the passage direction. Instead of tracking the person as a whole object, the system detects the presence of persons in each zone independently and determines passage based on the sequence of zone detections, thereby avoiding the harmful effects of environmental variations on overall image tracking.
2Reliability
If constant entrance detection is performed in each zone to improve accuracy, then determination reliability increases, but processing time and system complexity increase
Solution Approach 1:
The system performs detection in multiple zones along the passageway rather than continuous tracking throughout the entire passage. By detecting person presence in each zone at discrete points and analyzing the temporal sequence of detections, the system achieves reliable passage determination with reduced processing requirements compared to constant full-path tracking.
3Measurement precision
If extensive setting or learning is performed for each capture environment to improve detection accuracy, then measurement precision increases, but device complexity and setup time increase
Solution Approach 1:
The detection system uses a universal approach that works across different capture environments without requiring environment-specific training or calibration. The zone-based detection method and temporal sequence analysis are applicable to various lighting conditions, backgrounds, and environmental factors, eliminating the need for extensive setting or learning for each environment.
Data Source
AI summary
This information processing device comprises: a detection unit that detects a foreign object which is not included in an image of a normal state, such detection performed in each of a plurality of zones set for a passageway in the image in which the passageway is captured from above; and a determination unit that determines whether a person corresponding to the foreign object has passed through the passageway on the basis of a change over time in the zone where the foreign object was detected.