Incremental Subwindow Object Detection for Real-Time Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional object detection methods in images are computationally intensive and not adaptable to dynamic speed/detection rate tradeoffs, making them unsuitable for real-time applications and contexts where only partial analysis is necessary.
Innovation Solution
A system that evaluates subwindows of an image based on their likelihood of containing an object, using incremental evaluation steps and storing information for subsequent evaluation, allowing for dynamic resource allocation and focusing on promising subwindows, with the option to fully evaluate high-likelihood subwindows and discard low-likelihood ones, utilizing an incrementally evaluatable detector like a neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential object detection methods are used to analyze all subwindows at all scales and orientations, then comprehensive object detection accuracy is improved, but computational time and processing speed deteriorate significantly
Solution Approach 1:
The patent divides the image into multiple subwindows and processes them in parallel using multiple processors. Each processor handles specific subwindows independently, allowing simultaneous evaluation of multiple regions at different scales and orientations without sequential processing delays.
Solution Approach 2:
The patent performs preliminary evaluation of subwindows using a first detector to identify promising regions before applying more computationally intensive second detectors. This preliminary filtering reduces the number of subwindows requiring full analysis, maintaining detection accuracy while reducing overall computational time.
2Reliability
If all subwindows are fully evaluated to ensure comprehensive object detection, then detection completeness is improved, but computational resources and processing overhead increase
Solution Approach 1:
The patent applies partial evaluation to subwindows based on their likelihood of containing objects. Promising subwindows receive full evaluation with multiple detectors, while less promising subwindows receive reduced evaluation or are discarded after preliminary assessment, optimizing resource allocation while maintaining detection reliability.
Solution Approach 2:
The patent applies different evaluation strategies to different subwindows based on local characteristics. Subwindows with features suggesting high object probability undergo comprehensive analysis, while others receive minimal processing, allowing computational resources to be concentrated where they provide maximum value.
3Speed
If the detection process is interrupted early to reduce processing time, then speed is improved, but detection rate and number of objects found decrease
Solution Approach 1:
The patent dynamically adjusts the detection process based on time constraints and detection goals. The system can interrupt evaluation of individual subwindows when time limits are approached, while maintaining parallel processing of other subwindows. Detection thresholds and evaluation depths are adjusted dynamically to balance speed and detection rate based on application requirements.
Data Source
AI summary
One embodiment of the present invention provides a system for detecting an occurrence of an object in an image. During operation, the system selects a subwindow to be evaluated based upon a currently estimated likelihood of the object being detected within the subwindow. The system then performs an evaluation step on the subwindow to determine if the object exists in the subwindow, wherein performing the evaluation step involves updating the currently estimated likelihood of the object being detected in the subwindow. If evaluation of the subwindow is not complete after performing the evaluation step, the system stores information associated with the subwindow to facilitate subsequent evaluation of the subwindow.