Image Analysis Area Determination Using Pose Prediction Data
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Solution Overview
Problem
Existing 3D pose reconstruction techniques face challenges in accuracy, speed, and power efficiency, particularly when objects are crowded or occluded, and when dealing with noisy images.
Innovation Solution
The proposed solution involves a system and method for image processing that uses pose prediction data to improve feature detection in 2D images, enabling faster and more power-efficient 3D pose reconstruction. This is achieved by processing devices that determine analysis areas based on predicted poses, allowing for more efficient detection of predefined features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional feature detection is performed on entire images without pose prediction, then detection completeness is maintained, but processing speed and power efficiency deteriorate
Solution Approach 1:
The system performs preliminary pose prediction using prediction data from previous time steps before conducting feature detection. This preliminary action defines a region of interest (ROI) based on predicted object location, allowing the subsequent feature detection to be focused only on this restricted area rather than processing the entire image, thereby improving processing speed and power efficiency while maintaining detection accuracy through the predicted pose guidance
Solution Approach 2:
The system applies different processing strategies to different regions of the image: high-resolution feature detection is performed only within the predicted region of interest, while other areas receive minimal or no processing. This local differentiation optimizes resource allocation by concentrating computational power where it is most needed (in the predicted object location) while reducing overall processing load
2Reliability
If feature detection is performed on crowded and occluded objects, then detection completeness improves, but processing complexity and computational load increase
Solution Approach 1:
The system uses pose prediction from previous time steps to pre-define the region of interest before feature detection. This preliminary action provides a head start in locating objects, even crowded or occluded ones, by narrowing down the search space to the predicted location. This reduces the computational complexity of detecting features in difficult scenarios while maintaining reliable detection through the guidance of predicted pose information
3Measurement precision
If full image processing is performed for feature detection, then detection accuracy is maintained, but power consumption increases
Solution Approach 1:
The system performs preliminary pose prediction using low-power prediction algorithms that leverage prediction data from previous time steps. This preliminary action establishes a region of interest that guides subsequent feature detection, allowing the system to maintain detection accuracy by focusing processing only on the predicted object location while significantly reducing overall power consumption by avoiding full-image processing
Solution Approach 2:
The system applies high-quality feature detection algorithms only to the predicted region of interest rather than the entire image. This local application of intensive processing maintains detection accuracy within the ROI while reducing total power consumption by excluding the majority of the image from expensive processing operations
Data Source
AI summary
A processing device is configured to obtain a sequence of images of a scene captured by an image sensor, determine an analysis area for an object in a respective image in the sequence of images, and process the respective image within the analysis area for detection of predefined features of the object. The processing device is further configured to receive pose prediction data, PPD, which represents predicted poses of the object as a function of time, and to determine the analysis area based on the PPD. The PPD may be given by three-dimensional poses of the object that have been determined in the system based on images from a plurality of image sensors in the system. The PPD facilitates detection of features of individual objects in the images even if the objects are occluded and/or crowded.