Image Tracking Action Detection With Weight-Sensor Event Triggers
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Solution Overview
Problem
Existing object tracking systems face challenges in large physical spaces due to the need to process information from multiple cameras independently and combine it to track objects across a larger area, which is computationally intensive and not suitable for real-time applications, and lack the ability to determine precise physical locations of objects.
Innovation Solution
A tracking system that generates relationships between camera pixels and physical locations using homographies, calibrates shelf positions, hands off tracking information between sensors, detects interactions with virtual curtains, associates items with persons, and identifies misplaced items using weight sensors and predefined zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple cameras are used to track objects in large physical spaces, then the tracking coverage area is improved, but the computational complexity increases
Solution Approach 1:
The system divides the large physical space into multiple zones, each monitored by a separate camera. Each camera independently processes objects within its own zone, generating zone-specific tracking data. This segmentation allows the system to cover large areas without requiring one camera to process the entire space, thereby reducing computational complexity while maintaining extensive coverage.
Solution Approach 2:
The system adds a zone dimension to the tracking system by introducing zone identifiers that associate each object with a specific camera zone. This dimensional extension allows the central processor to receive pre-organized data from multiple cameras, reducing the computational burden of processing raw data from all cameras simultaneously while maintaining comprehensive coverage.
2Speed
If multiple cameras independently process tracking data, then real-time processing capability is improved, but the ability to track objects across the entire space deteriorates
Solution Approach 1:
The system introduces a central processor as an intermediary that receives tracking data from multiple independent cameras. This intermediary consolidates zone-specific tracking information, maintains object identities across zone boundaries, and ensures continuous tracking. The intermediary enables real-time processing by cameras while preventing information loss through centralized coordination.
Solution Approach 2:
By adding the zone identifier dimension to tracking data, the system enables cameras to process data independently within their zones while the central system maintains cross-zone tracking continuity. This dimensional approach preserves real-time processing capabilities while preventing object tracking information loss across camera boundaries.
3Measurement precision
If conventional object identification methods are used in busy environments, then identification accuracy is improved, but processing time increases significantly
Solution Approach 1:
The system segments the identification process by having each camera independently identify objects within its own zone using zone-specific data. This segmentation allows parallel processing of multiple identification tasks simultaneously, reducing overall processing time while maintaining identification accuracy through localized analysis.
Solution Approach 2:
Each camera performs identification on a subset of objects within its zone rather than processing all objects in the entire space. This partial action approach reduces processing time by dividing the workload, while the central processor ensures comprehensive identification coverage by aggregating results from all zones.
Data Source
AI summary
A system includes a sensor, a weight sensor, and a tracking subsystem. The tracking subsystem receives an image feed of top-view images generated by the sensor and weight measurements from the weight sensor. The tracking subsystem detects an event associated with an item being removed from a rack in which the weight sensor is installed. The tracking subsystem determines that a first person or a second person may be associated with the event. In response to determining that the first or second person may be associated with the event, buffer frames are stored of top-view images generated by the sensor during a time period associated with the event. The tracking subsystem then determines, using at least one of the stored buffer frames and a first action-detection algorithm, whether an action associated with the event was performed by the first person or the second person.