Pedestrian Detection via Multi-Stage Window Calibration
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Solution Overview
Problem
Conventional monitoring cameras face difficulties in accurately tracing pedestrians due to environmental interference and varying pedestrian positions or angles, leading to decreased detection accuracy and efficiency.
Innovation Solution
A pedestrian detection method that forms multiple detecting windows within a monitoring frame using object analysis, human form detection, and human local detection functions to reduce the search area and calibrate the detection windows for precise head or face identification, enabling accurate detection even when pedestrians are partially obscured or at unusual angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring camera uses human face detection or human form detection to scan the entire monitoring frame, then it can trace pedestrians, but the operation is complicated and detection accuracy decreases due to environmental interference
Solution Approach 1:
The detection process is segmented into multiple stages: first detecting window for initial pedestrian detection, second detecting window for refined detection, and third detecting window for head/face detection. Each stage focuses on a smaller, more specific region, reducing computational complexity while improving accuracy. The segmentation divides the monitoring frame into progressively smaller detection regions, making the overall system more manageable and accurate.
Solution Approach 2:
The system performs preliminary detection actions by first identifying potential pedestrian regions using the first detecting window before conducting more detailed analysis. This preliminary action filters out non-pedestrian regions early in the process, reducing the search space for subsequent detection stages and improving overall detection accuracy while reducing operational complexity.
2Adaptability or versatility
If the monitoring camera is fixed and scans the entire monitoring frame, then it can detect pedestrians at any position, but the detection efficiency decreases when pedestrians are partially obscured or at unusual angles
Solution Approach 1:
The detection windows dynamically adjust their size and position based on the detected pedestrian characteristics. The first detecting window adapts to locate pedestrians anywhere in the frame, the second detecting window refines the detection region based on initial findings, and the third detecting window further adapts to focus on head/face regions. This dynamic adjustment maintains detection coverage while significantly improving efficiency.
Solution Approach 2:
Different detection windows apply different detection strategies to different regions of the monitoring frame. The first detecting window uses broad pedestrian detection, the second uses refined form detection, and the third uses specialized head/face detection. This local quality approach ensures high detection efficiency for each region type while maintaining overall adaptability across the entire monitoring frame.
3Measurement precision
If the monitoring camera uses multiple detection functions to improve accuracy, then detection precision increases, but the computational load increases requiring more hardware resources
Solution Approach 1:
The computational task is segmented across three detecting windows that process progressively smaller regions. The first detecting window handles broad pedestrian detection, the second handles refined form detection in a smaller region, and the third handles head/face detection in an even smaller region. This segmentation reduces the total computational energy required compared to applying a single comprehensive detection function to the entire monitoring frame.
Solution Approach 2:
The system applies detection functions selectively rather than uniformly across the entire frame. By using partial action - applying detailed head/face detection only to regions where pedestrians are already detected by previous windows - the system achieves high detection accuracy while minimizing unnecessary computational energy expenditure on regions without pedestrians.
Data Source
AI summary
A pedestrian detection method is applied to a monitoring camera. The pedestrian detection method includes forming a first detecting window on at least one monitoring frame via an object analysis function, utilizing a human form detection function to modulate the first detecting window for forming a second detecting window, analyzing the second detecting window via a human local detection function to mark an upper detecting window about a pedestrian on the monitoring frame, and determining whether to calibrate the second detecting window via analysis of the upper detecting window.