Object Detection via Dynamic Region of Interest Selection
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Solution Overview
Problem
Current object detection methods in images and videos are resource-intensive, particularly when evaluating entire frames for the presence of objects, as many sections do not contain objects, leading to inefficient use of resources.
Innovation Solution
Implementing a system that uses a neural network to perform object detection only within a selected region of interest (ROI) based on previous frames, dividing the image into sections, and adjusting the ROI dynamically to encompass only relevant areas, thereby reducing processing resources needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection is performed on entire frames, then detection accuracy is maintained, but computational resources and power consumption increase significantly
Solution Approach 1:
The patent divides video frames into multiple sections or regions, and performs object detection only on selected regions of interest (ROIs) rather than entire frames. This segmentation approach maintains detection accuracy for objects while significantly reducing the computational area and power consumption by excluding irrelevant frame portions from processing.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the frame. High-quality object detection is applied only to ROIs where objects are likely to be present, while other regions receive minimal or no processing. This local quality differentiation preserves detection accuracy where needed while reducing overall power consumption.
2Reliability
If object detection is performed on entire frames, then all objects are detected, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by analyzing previous frames to identify regions where objects are likely to be present in current frames. This preliminary identification of ROIs based on temporal information allows the system to focus detection resources on relevant areas, ensuring objects are detected while improving processing efficiency by avoiding unnecessary full-frame analysis.
Solution Approach 2:
The patent uses feedback from previous frame analysis to guide current frame processing. By continuously updating ROI selections based on detected objects in previous frames and their motion patterns, the system maintains detection completeness while optimizing processing efficiency through adaptive region selection.
3Reliability
If full frame processing is used, then no objects are missed, but bandwidth and data transmission requirements increase
Solution Approach 1:
The patent extracts and processes only the essential portions of frames (ROIs) that contain or are likely to contain objects, rather than transmitting and processing entire frames. This extraction approach maintains object detection reliability by focusing on relevant data while significantly reducing bandwidth requirements by excluding redundant frame portions from transmission and processing.
Data Source
AI summary
Apparatuses, systems, and methods to cause one or more locations of one or more objects within one or more images to be identified based, at least in part, on one or more locations of the one or more objects within one or more previous images.