Image Processing System for Real-Time Object Detection
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Solution Overview
Problem
Existing image processing systems face challenges in efficiently detecting and classifying objects at varying distances and types, particularly as objects move further away, requiring increased resources and being limited to pre-trained object types.
Innovation Solution
An image processing system comprising a localizer and a classifier, where the localizer uses a neural network to identify object locations in low-resolution images independently of object class, and the classifier analyzes these locations in higher-resolution images, allowing for real-time inference of moving objects with minimal resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-resolution image is used for object detection, then object detection accuracy is improved, but computational resources required increase significantly
Solution Approach 1:
The system segments the object detection task into two distinct stages: a low-resolution stage for detecting object presence and location, and a high-resolution stage for classification. This segmentation allows the computationally intensive classification to be performed only on a subset of detected objects, significantly reducing overall resource requirements while maintaining detection accuracy.
Solution Approach 2:
The system applies partial action by using low-resolution images for the initial detection phase, which is sufficient for identifying object presence and location but not for detailed classification. This partial application of high-resolution processing only where needed optimizes the balance between accuracy and computational resources.
2Measurement precision
If multiple classifiers are applied for different object types, then object classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by first detecting objects and their locations using a low-resolution image before applying classification. This preliminary detection stage filters out non-object regions, allowing subsequent classification to be performed only on relevant areas, thus reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The classification process is segmented by applying it only to objects detected in the low-resolution image, rather than processing the entire high-resolution image for all possible object types. This segmentation dramatically reduces the number of classification operations required.
3Length of stationary object
If objects are detected at long distances, then detection range is improved, but object size in image decreases making detection difficult
Solution Approach 1:
The system segments the detection process into two resolution levels: low-resolution for detecting distant objects where they appear small, and high-resolution for analyzing objects that have been detected. This allows the system to maintain the ability to detect distant objects while having sufficient detail for accurate classification.
Solution Approach 2:
The system transitions from a single-resolution approach to a multi-resolution approach, adding the resolution dimension as a variable that can be adapted to the distance of detected objects. This dimensional addition allows simultaneous optimization of detection range and classification accuracy.
Data Source
AI summary
An image processing system is configured to receive a first high resolution stream of images and a second lower resolution stream of images from image sources with substantially the same field of view. The system comprises a localizer component configured to provide a location for any object of interest independently of class within successive images of the second stream of images; a classifier configured to: receive one or more locations selectively provided by the localizer, identify a corresponding portion of an image acquired from the first stream at substantially the same time at which an image from the second stream in which an object of interest was identified and return a classification for the type of object within the identified portion of the image from the first stream; and a tracker configured to associate the classification with the location through acquisition of successive images in the second stream.