Multimodal Integral Filter for Visual Object Detection
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Solution Overview
Problem
Current object detection methods in computer vision face inefficiencies and inaccuracies due to the inability to effectively remove irrelevant data and handle crowded scenes, leading to false alarms and reduced detection accuracy.
Innovation Solution
The method employs multimodal integral filters to pre-process images by removing irrelevant portions and performing temporal non-maximum suppression on multiple frames to reduce duplicate detections and false positives, utilizing integral images and face-like background filtering to enhance detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object detection methods are used without filtering, then all regions in the image are processed for detection, but this leads to increased computational complexity and false alarms due to irrelevant data
Solution Approach 1:
The patent applies integral image filtering and background subtraction before object detection to pre-process and remove irrelevant regions. This preliminary action eliminates unnecessary computational work on background areas, reducing the complexity of the main detection process while improving reliability by focusing only on relevant foreground regions.
Solution Approach 2:
The patent extracts and removes background regions from the image using integral images and background subtraction techniques. By taking out the irrelevant background data before detection, the system reduces false alarms and computational complexity, processing only the remaining foreground regions of interest.
2Reliability
If non-maximum suppression is applied to each frame individually, then duplicate detections within a single frame are reduced, but detections across multiple frames are not properly handled leading to temporal redundancy
Solution Approach 1:
The patent merges the non-maximum suppression process across multiple frames by maintaining detection states temporally. Instead of applying NMS independently to each frame, the system combines detection results from multiple frames and applies suppression across the temporal sequence, reducing redundant detections while maintaining consistency.
Solution Approach 2:
The patent introduces dynamic temporal tracking of detection states across frames. The non-maximum suppression adapts to temporal dynamics by considering the persistence and movement of detected objects across multiple frames, allowing the system to distinguish between genuine objects and transient false detections.
3Reliability
If integral images are computed for all image regions, then comprehensive detection coverage is achieved, but this increases computational load and processing time
Solution Approach 1:
The patent extracts and removes background regions using integral image-based background subtraction before applying comprehensive detection. By taking out the large background portions that don't contain objects of interest, the system maintains detection coverage for relevant regions while significantly reducing the computational load and processing time.
Solution Approach 2:
The patent segments the image into background and foreground regions using integral images. By dividing the image processing into separate stages—first identifying and removing background, then applying detailed detection only to foreground regions—the system achieves comprehensive coverage where needed while improving overall processing efficiency.
Data Source
AI summary
Machine logic that pre-processes and post-processes images for visual object detection by performing the following steps: receiving a set of image(s); filtering the set of image(s) using a set of multimodal integral filter(s), thereby removing at least a portion of the set of image(s) and resulting in a filtered set of image(s); performing object detection on the filtered set of image(s) to generate a set of object-detected image(s); assembling a first plurality of object-detected image(s) from the set of object-detected image(s); and upon assembling the first plurality of object-detected image(s), performing non-maximum suppression on the assembled first plurality of object-detected image(s).