Pedestrian Detection Using Critical Area Segmentation
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Solution Overview
Problem
Traditional pedestrian detection systems fail to produce optimal results in challenging urban scenarios, particularly when pedestrians are hidden behind environmental barriers like stopped vehicles, as they are adapted to recognize generic urban environments and detect pedestrians in large areas ahead of the vehicle.
Innovation Solution
A pedestrian detection apparatus that uses a critical area detection unit to identify preset environments and areas surrounding moving obstacles, employing a method that corrects distance data, clusters and merges segments, classifies obstacles, and focuses on specific areas to detect pedestrians, thereby enhancing detection accuracy and safety in urban environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional pedestrian detection systems search large areas ahead of the vehicle, then the coverage area is improved, but the detection precision in critical areas deteriorates
Solution Approach 1:
The patent divides the detection area into multiple zones based on distance from the vehicle: a first detection area (closer to vehicle) and a second detection area (farther from vehicle). Different detection parameters and algorithms are applied to each zone, allowing optimized precision in critical near areas while maintaining broad coverage in distant areas.
Solution Approach 2:
The system applies different detection strategies to different spatial regions. In the first detection area (critical zone), more intensive processing with higher precision parameters is used. In the second detection area, lighter processing with lower precision parameters is applied, optimizing overall system performance by matching detection quality to local risk levels.
2Area of stationary object
If pedestrian detection is performed in large areas, then the detection coverage is improved, but the computational complexity increases
Solution Approach 1:
The detection space is segmented into multiple areas with different processing requirements. By dividing the large detection area into zones, the system reduces computational complexity in each zone while maintaining overall broad coverage, avoiding the need to apply full computational resources across the entire large area.
Solution Approach 2:
The system applies partial detection action to different areas - full precision processing is applied only to critical near areas (first detection area), while reduced precision processing is applied to distant areas (second detection area). This partial application of computational resources reduces overall complexity while maintaining safety in critical zones.
3Area of stationary object
If environmental barriers like stopped vehicles are present, then the detection coverage is improved, but the detection precision in hidden areas deteriorates
Solution Approach 1:
The system performs preliminary detection of environmental barriers (stopped vehicles, obstacles) before conducting pedestrian detection. By first identifying barriers that may hide pedestrians, the system can then focus subsequent detection efforts on areas behind or near these barriers, improving precision in previously hidden areas through targeted search strategies.
Solution Approach 2:
When environmental barriers are detected, the system locally adjusts detection parameters in areas behind or near the barriers. These critical zones receive enhanced detection attention and different processing parameters compared to open areas, improving precision for pedestrians who may be partially occluded or hidden by the barriers.
Data Source
AI summary
An apparatus, method for detecting critical areas and a pedestrian detection apparatus using the same are provided. An application of the pedestrian detection system is provided to help limit critical urban environment to particular areas. Contrary to traditional pedestrian detection systems that localize every pedestrians appearing in front of the subject vehicle, the apparatus first finds critical areas from urban environment and performs a focused search of pedestrians. The environment is reconstructed using a standard laser scanner but the subsequent checking for the presence of pedestrians is performed by incorporating a vision system. The apparatus identifies pedestrians within substantially limited image areas and results in boosts of timing performance, since no evaluation of critical degrees is necessary until an actual pedestrian is informed to the driver or onboard computer.