Vehicle Object Detection with Block-Based Person Search
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Solution Overview
Problem
Existing object detection devices for vehicles have a high processing load due to the large areas in which person detection processes are performed, leading to potential delays in detecting obstacles and increased computational requirements.
Innovation Solution
The object detection device partitions the XY-plane into specific blocks corresponding to the vehicle's path, focusing the person detection process on neighboring objects within these blocks, thereby reducing the processing load and improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If person detection process is performed on a large area, then detection coverage is improved, but processing load increases
Solution Approach 1:
The detection area is divided into multiple blocks, and person detection is performed selectively in specific blocks rather than the entire area. This segmentation allows the system to maintain comprehensive detection coverage while reducing the total processing load by focusing computational resources only on relevant regions.
Solution Approach 2:
Different blocks are assigned different detection priorities based on their relevance to the vehicle's path. Blocks containing neighboring objects that may block the vehicle are subjected to person detection, while other blocks are excluded. This local quality approach ensures detection resources are concentrated where they are most needed.
2Reliability
If person detection process is performed on a large area, then detection completeness is improved, but detection time increases
Solution Approach 1:
The detection area is segmented into multiple blocks, enabling parallel processing of different regions. By performing person detection only in blocks containing neighboring objects, the system reduces total detection time while maintaining completeness for critical areas through selective focus.
Solution Approach 2:
The system performs preliminary identification of neighboring objects and their spatial relationships before conducting person detection. This preliminary action allows the system to pre-determine which blocks require detailed person detection, thereby avoiding unnecessary processing time in irrelevant areas while ensuring complete detection where needed.
Data Source
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AI summary
An object detection device (41) configured to be mounted on a vehicle (10) includes an object extraction unit that is configured to extract a point group that is a set of points representing a part of an object (O) as the object (O), a neighboring object extraction unit that is configured to extract a neighboring object (O1) in an XY-plane of a world coordinate system, wherein the neighboring object (O1) is the object (O) which is the closest to the vehicle (10), a coordinate transformation unit that is configured to transform coordinates of the neighboring object (O1) in the world coordinate system into coordinates of the neighboring object (O1) in an image (11) captured by a camera (31), and a person determination unit that is configured to perform a person detection process in which it is determined whether or not the neighboring object (O1) is a person on the coordinates of the neighboring object (O1).