Dynamic Image Cropping for Reliable Shopping Event Detection
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Solution Overview
Problem
Detecting and locating objects or actors in dynamic environments using cameras is computationally expensive and requires large amounts of data processing, which can be inefficient and time-consuming, especially when cropping images without proper selection of the cropped portions.
Innovation Solution
Implementing a system that dynamically crops images based on points corresponding to product spaces and body part features, using machine learning models to process and predict events such as taking or returning items, reducing data processing by focusing on relevant image segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large numbers of cameras capture imaging data to detect and locate objects or actors in dynamic environments, then detection coverage and reliability are improved, but data processing requirements and computational costs increase significantly
Solution Approach 1:
The patent segments the imaging data processing task by dividing it into multiple stages: initial full-image capture, dynamic cropping to focus on relevant regions, and then event detection on the cropped segments. This segmentation reduces the overall computational burden while maintaining detection reliability by ensuring that critical regions are analyzed in detail.
Solution Approach 2:
The patent extracts and focuses computational resources on the most relevant portions of the images by dynamically cropping out regions containing actors or objects of interest. This extraction principle allows the system to process only the necessary data portions rather than analyzing entire high-resolution images, significantly reducing processing requirements while preserving detection accuracy.
2Measurement precision
If full-resolution images are processed to detect events and locate objects, then measurement precision and detection accuracy are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies dynamics by implementing adaptive, dynamic cropping that adjusts the region of interest based on detected actors or objects in real-time. Rather than using fixed crop regions, the system dynamically modifies the analysis area according to the scene content, maintaining high measurement precision for relevant objects while reducing processing time by excluding irrelevant areas from detailed analysis.
3Productivity
If images are cropped to reduce data processing requirements, then processing efficiency is improved, but detection accuracy may deteriorate if inappropriate portions are cropped
Solution Approach 1:
The patent implements feedback mechanisms where the cropping process is guided by detection results from previous frames or preliminary analysis. The system uses feedback from actor detection, object detection, or motion analysis to dynamically adjust crop regions, ensuring that areas containing relevant events are preserved while non-critical areas are excluded. This feedback loop maintains detection accuracy while achieving processing efficiency.
Data Source
AI summary
Systems within materials handling facilities or retail establishments are programmed to receive images from cameras, process clips of the images to generate sets of features representing product spaces and actors depicted within such images, and to classify the clips as depicting or not depicting a shopping event. The images are dynamically cropped to reduce amounts of data that must be processed to in order to determine whether the images depict shopping events. The images are cropped by calculating a center point based on positions of points on product spaces and detected overlaps of hands and the product spaces. Features of clips determined to depict a shopping event are combined into a sequence and transferred, along with classifications of such clips, to a multi-camera system that generates a shopping hypothesis based on such features.