Object Detection Histogram Analysis Speed
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Solution Overview
Problem
Existing object detection systems face challenges with prolonged calculation times and false positives due to inefficient indexing and uncertainty in object position, leading to time-consuming detection and potential false identification of objects.
Innovation Solution
An object detection apparatus that reduces calculation load by determining average dictionary complexities, maximum search pixel skipping amounts, and search window size changes, allowing for faster and more accurate detection by analyzing histograms and adjusting dictionary complexities and search window sizes based on normal distribution patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If histogram analysis is performed on edge extraction frequencies in both horizontal and vertical directions, then object detection accuracy is improved, but calculation time is prolonged
Solution Approach 1:
The patent segments the histogram analysis by performing it only in the horizontal direction rather than both horizontal and vertical directions. This segmentation reduces the calculation scope while maintaining detection accuracy by focusing on the most critical directional information for object detection.
Solution Approach 2:
The patent extracts and utilizes only the essential histogram information from edge extraction frequencies in the horizontal direction, discarding the redundant vertical direction analysis. This extraction of key features maintains detection effectiveness while reducing computational burden.
2Reliability
If a complex classifier with multiple stages is used, then detection rate and false positive reduction are improved, but detection speed decreases
Solution Approach 1:
The patent applies preliminary action by using a pixel skipping mechanism that pre-filters potential object regions before applying the full classifier. This preliminary filtering reduces the number of regions that require complex classification, thereby maintaining accuracy while improving detection speed.
Solution Approach 2:
The patent implements partial action by applying the complex classifier only to selected regions identified by the histogram analysis and pixel skipping mechanism, rather than to the entire image. This partial application maintains detection reliability while significantly reducing processing time.
3Adaptability or versatility
If the search window size is increased to cover uncertain object positions, then detection coverage is improved, but calculation load increases
Solution Approach 1:
The patent applies dynamics by making the search window size adaptive rather than fixed. The search window size is dynamically adjusted based on the histogram analysis results and detected object characteristics, allowing the system to cover uncertain positions when needed while reducing calculation load when object positions are well-defined.
Data Source
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AI summary
An object detection apparatus (1) for detecting an object from an image obtained by taking a front view picture of a road in a traveling direction of a vehicle includes a camera unit (10) for taking the front view picture of the road and inputting the image; a dictionary modeling the object; a search unit for searching the image with a search window; a histogram production unit for producing a histogram by comparing the image in the search window with the dictionary and counting a detection frequency in a direction parallel to a road plane; and a detection unit (22) for detecting the detection object by detecting a unimodal distribution from the histogram.