Behavioral Model Update via Multi-Camera Path Tracking
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Solution Overview
Problem
Existing systems fail to accurately track and analyze the movement patterns of individuals within multi-camera monitored physical spaces, as they lack the capability to seamlessly integrate data from cameras with non-overlapping fields of view and provide detailed behavioral models.
Innovation Solution
A computing device equipped with an image processing engine and a path processing engine that identifies individuals across multiple camera feeds with different fields of view, determines their paths, and updates a behavioral model based on their movements, incorporating metadata and purchase data to classify visitor behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple cameras with different fields of view are used to monitor physical spaces, then the coverage area increases, but the difficulty of detecting and measuring individual movement patterns increases
Solution Approach 1:
The system divides the physical space into multiple zones, each monitored by specific cameras. Individuals are tracked by segmenting their movement path into discrete zone transitions, making it easier to analyze movement patterns across large areas covered by multiple cameras with different fields of view.
Solution Approach 2:
The system uses an intermediary processing layer that correlates images from multiple cameras by identifying unique individual characteristics and matching them across camera fields of view. This intermediary processing enables seamless tracking of individuals as they move between different camera zones without requiring direct overlap between camera views.
2Measurement precision
If detailed behavioral models are created by integrating data from multiple cameras, then the measurement precision of movement patterns improves, but the device complexity increases
Solution Approach 1:
The system employs a universal image processing engine that handles multiple camera inputs and performs various analytical functions including individual identification, path determination, and behavioral model updating. This multi-functional approach consolidates complexity into a single processing system rather than requiring separate systems for each camera or function.
Solution Approach 2:
The system continuously updates behavioral models based on observed movement patterns and uses this feedback to improve future tracking accuracy. The behavioral models store and analyze transitions between zones, providing feedback that refines the precision of movement pattern analysis while managing system complexity through iterative learning.
Data Source
AI summary
Examples disclosed herein relate to a person moving in a physical space. In one aspect, a method is disclosed. The method may include obtaining at least two images of a person from at least two cameras directed at a physical space, where the physical space may include a plurality of designated areas. The method may also include obtaining metadata associated with the images, based on the images and the metadata determining within the plurality of designated areas a set of designated areas visited by the person, for each designated area within the set of designated areas, determining an area information, and updating a database based on the set of designated areas and based on at least a portion of the area information associated with each designated area within the set of designate areas.