Adaptive Sampling for Faster Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional object detection methods are computationally expensive and time-consuming due to the extraction of a large number of features from images, which hinders speed and efficiency in object detection processes.
Innovation Solution
The implementation of adaptive sampling techniques to extract coarse features from images, generating an object probability map, and then extracting dense features from high-probability regions, combined with cascade object detection using subsets of exemplars to validate object candidate regions, significantly reduces the number of features extracted and accelerates the detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods extract a large number of multi-scale features from an image to enable object detection, then detection accuracy is improved, but processing time increases significantly making it the bottleneck of the detection process
Solution Approach 1:
The patent segments the feature extraction process into two distinct stages: coarse feature extraction followed by dense feature extraction. Coarse features are extracted at a lower resolution to quickly identify potential object regions, then dense features are extracted only from those high-probability regions. This segmentation reduces the total number of features extracted while maintaining detection accuracy in critical areas.
Solution Approach 2:
The patent applies local quality by differentiating the quality and density of features extracted from different regions of the image. High-probability object regions receive dense, high-quality feature extraction, while low-probability regions receive coarse or no feature extraction. This selective approach optimizes the balance between accuracy and processing time by concentrating computational resources where they are most needed.
2Measurement precision
If a large collection of exemplars is used as classifiers to recognize objects, then detection accuracy is improved, but computational cost increases as similarity calculation between each test region and each exemplar becomes expensive
Solution Approach 1:
The patent performs preliminary action by first extracting coarse features and generating an object probability map before applying exemplar-based classification. This preliminary step identifies high-probability object regions, allowing the system to limit subsequent expensive exemplar similarity calculations to only those regions likely to contain objects, rather than computing similarities across the entire image.
Solution Approach 2:
The patent applies partial action by using a subset of exemplars for initial classification and reserve exemplars for validation of candidate regions. Rather than applying all exemplars to all regions, the system selectively applies exemplars based on the object probability map, performing classification only where necessary to maintain accuracy while reducing overall computational burden.
Data Source
AI summary
Accelerating object detection techniques are described. In one or more implementations, adaptive sampling techniques are used to extract features from an image. Coarse features are extracted from the image and used to generate an object probability map. Then, dense features are extracted from high-probability object regions of the image identified in the object probability map to enable detection of an object in the image. In one or more implementations, cascade object detection techniques are used to detect an object in an image. In a first stage, exemplars in a first subset of exemplars are applied to features extracted from the multiple regions of the image to detect object candidate regions. Then, in one or more validation stages, the object candidate regions are validated by applying exemplars from the first subset of exemplars and one or more additional subsets of exemplars.