People Detection with Orientation-Aware Classifiers for Fisheye Cameras
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Solution Overview
Problem
Existing people detection systems using overhead cameras, especially those with fisheye lenses, face challenges such as occlusion, distortion, and resource inefficiency due to the need for rotation and scaling transforms, which are orientation and rotation-dependent and consume significant memory and computation resources, making them unsuitable for embedded processors.
Innovation Solution
The system employs multiple classifiers that are orientation and size-aware, selecting the appropriate classifier based on the position and orientation of the person within the image, reducing the need for transforms and optimizing resource usage by associating different classifiers with various positions and orientations, and dynamically adjusting the stride for sliding window detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rotation and scaling transforms are applied to handle fisheye lens views, then detection accuracy is improved, but memory and computation resources are significantly consumed
Solution Approach 1:
The image is divided into multiple regions (e.g., center region, corner regions) with different transform requirements. Only specific regions undergo rotation and scaling transforms, while others use simplified detection, reducing overall computational load while maintaining accuracy where needed.
Solution Approach 2:
Different detection strategies are applied to different regions of the image. High-accuracy transform-based detection is applied only to critical regions, while other regions use lighter-weight detection methods, optimizing the balance between accuracy and resource consumption.
2Measurement precision
If rotation and scaling transforms are applied to handle fisheye lens views, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The complex transform operation is segmented into region-specific operations. Different regions use different levels of transformation complexity, simplifying the overall system architecture while maintaining necessary accuracy in critical areas.
Solution Approach 2:
The system dynamically selects detection strategies based on region characteristics. Transform parameters and detection methods are adjusted dynamically according to the specific region being processed, reducing the need for complex fixed-architecture transforms throughout the entire image.
3Measurement precision
If multiple classifiers are used for different positions and orientations, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
Different classifiers with specialized features are applied to different spatial regions and orientation zones. Each classifier is optimized for its specific region, improving local detection accuracy while the overall system remains manageable through structured organization of classifiers by region.
Solution Approach 2:
The classifier set is segmented into region-specific groups (e.g., center classifiers, corner classifiers, orientation-specific classifiers). This segmentation allows the system to activate only the necessary classifiers for each region, reducing the effective complexity at any given time while maintaining high accuracy across the entire image.
4Device complexity
If a single classifier is used for all regions, then device complexity is reduced, but detection accuracy deteriorates
Solution Approach 1:
A hierarchical classifier structure is employed where a universal base classifier handles all regions, and region-specific specialized classifiers are applied only where needed. This multi-functionality approach maintains simplicity for most regions while providing enhanced accuracy for challenging regions.
Solution Approach 2:
Instead of applying complex region-specific classifiers to all regions (excessive action), the system applies simplified classifiers universally and adds complexity only partially where actually needed. This selective application of complexity maintains accuracy where required while minimizing overall device complexity.
Data Source
AI summary
This disclosure describes techniques to detect an object. The techniques include operations comprising: receiving an image captured by overhead camera; identifying a region of interest (ROI) of a plurality of regions within the image; selecting an object classifier from a plurality of object classifiers based on a position of the identified ROI relative to the overhead camera; and applying the selected object classifier to the identified ROI; and detecting presence of the object within the ROI in response to applying the selected object classifier to the identified ROI.