Actor-Event Association via 3D Vector Confidence in Crowded Scenes
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Solution Overview
Problem
In dynamic environments like materials handling facilities, it is challenging to accurately associate events with actors due to the complexity of recognizing and distinguishing between multiple actors in crowded spaces with varying orientations and movements, especially when digital cameras have fixed orientations and capture images with many people, objects, or machines of different sizes and velocities.
Innovation Solution
The system employs digital cameras configured to capture imaging data and processes it using machine learning techniques to determine which body parts of actors are associated with events, calculating confidence scores for each pixel and generating records of coordinate pairs and confidence scores, which are then aggregated by a server to identify the most likely actor involved in an event based on overlapping fields of view and image quality analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple digital cameras with fixed orientations are deployed to monitor dynamic environments, then the coverage area increases, but the difficulty of distinguishing between multiple actors increases
Solution Approach 1:
The patent transitions from 2D image analysis to 3D spatial reasoning by introducing depth information and 3D coordinate transformations. The system maps 2D pixel coordinates to 3D world coordinates using camera calibration parameters, enabling accurate depth estimation and spatial relationship analysis that resolves actor ambiguity in crowded scenes
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw image data and actor identification. This intermediary processing layer extracts meaningful features, estimates body part locations, and computes confidence scores, effectively mediating the complex task of distinguishing actors in multi-camera views
2Reliability
If digital cameras capture images of crowded environments with multiple actors, then the event detection capability improves, but the accuracy of associating events with specific actors decreases
Solution Approach 1:
The patent implements feedback mechanisms through confidence score computation and iterative refinement. The system calculates confidence scores for each potential actor-event association, uses this feedback to prioritize and refine analysis, and continuously improves association accuracy by comparing predicted body part locations with actual image features
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on scene complexity and camera viewing angles. The system modifies processing thresholds, confidence score weights, and body part detection sensitivity according to environmental conditions, optimizing the balance between event detection reliability and actor identification precision
3Measurement precision
If machine learning techniques are used to analyze imaging data, then the ability to identify body parts improves, but the computational complexity increases
Solution Approach 1:
The patent segments the complex task of actor identification into distinct components: body part detection, coordinate transformation, confidence score computation, and association matching. This segmentation allows each component to be optimized independently and processed in parallel, reducing overall computational complexity while maintaining high identification precision
Data Source
AI summary
Where an event is determined to have occurred at a location within a vicinity of a plurality of actors, imaging data captured using cameras having the location is processed using one or more machine learning systems or techniques operating on the cameras to determine which of the actors is most likely associated with the event. For each relevant pixel of each image captured by a camera, the camera returns a set of vectors extending to pixels of body parts of actors who are most likely to have been involved with an event occurring at the relevant pixel, along with a measure of confidence in the respective vectors. A server receives the sets of vectors from the cameras, determines which of the images depicted the event in a favorable view, based at least in part on the quality of such images, and selects one of the actors as associated with the event accordingly.