3D Point Cloud Processing for Moving Object Detection
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Solution Overview
Problem
Current methods for extracting 3D point clouds from images are computationally intensive and resource-heavy, particularly when dealing with scenes containing moving objects, as they require updating the entire point cloud with every frame, leading to inefficiencies and increased processing time.
Innovation Solution
The system identifies static and moving objects by creating a static reference frame using central pixel values from a series of frames, allowing for selective updating of the 3D point cloud only in regions with moving objects, thereby reducing computational load and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire point cloud is updated with every frame, then the accuracy of the 3D representation is maintained, but the computational resources and processing time increase significantly
Solution Approach 1:
The patent divides the scene into static and moving object regions by creating a static reference frame and comparing it with current frames. Only the point cloud regions corresponding to moving objects are updated, while static regions are retained from the reference frame. This segmentation approach maintains accuracy for both static and moving objects while significantly reducing processing requirements.
Solution Approach 2:
The patent applies different update strategies to different regions of the point cloud based on their motion characteristics. Moving object regions receive full updates from current frames, while static object regions use the pre-computed reference frame. This local differentiation optimizes both accuracy and processing efficiency by applying computational effort only where necessary.
2Reliability
If the entire point cloud is updated with every frame, then all objects are represented accurately, but the computational load increases
Solution Approach 1:
The patent segments the processing task by identifying which regions of the point cloud correspond to moving objects versus static objects. By using the static reference frame for unchanging regions, the system reduces computational resources spent on processing static areas while maintaining full updates only for moving object regions, thus optimizing energy usage without sacrificing representation accuracy.
Solution Approach 2:
Instead of performing complete point cloud updates for all regions, the patent applies partial updates only to moving object regions. This partial action approach reduces computational load and energy consumption while maintaining sufficient accuracy for the dynamic portions of the scene that require updates.
3Productivity
If selective updating is applied to moving objects only, then processing time is reduced, but the complexity of identifying static and moving objects increases
Solution Approach 1:
The patent performs preliminary action by pre-computing a static reference frame from initial frames before processing subsequent frames. This reference frame serves as a baseline for identifying moving objects in later frames, simplifying the identification process and reducing real-time computational complexity while enabling selective updates to moving object regions only.
Solution Approach 2:
The static reference frame acts as an intermediary between the raw image frames and the final point cloud representation. By comparing current frames against this intermediate reference, the system efficiently identifies moving objects without requiring complex direct analysis, thus reducing overall system complexity while enabling selective updating.
Data Source
AI summary
Generally discussed herein are systems, apparatuses, and methods for moving object detection. In one or more embodiments, a method can include computing a central value for each color of each pixel in a series of frames from a static imaging device to create a static reference frame, computing a difference between pixel values of a first frame captured after the series of frames by the static imaging device and a central value of a corresponding pixel value in the static reference frame, comparing the computed differences to a difference threshold, aggregating contiguous pixels corresponding to differences greater than the difference threshold into contiguous segments of pixels, and determining if the contiguous segments correspond to a moving object by registering the contiguous segments to pixels of a frame preceding the first frame.