Vehicle Vision System Pedestrian Detection Segmentation

The fish-eye image is divided into multiple regions (e.g., central region and peripheral regions) based on camera optics and orientation. Each region is processed using dedicated classifiers trained on region-specific characteristics, allowing the system to account for variations in pedestrian size and orientation across different parts of the image while maintaining manageable processing complexity through localized processing.

Different processing methods and classifiers are applied to different regions of the image. The central region uses one type of classifier while peripheral regions use different classifiers optimized for their specific viewing angles and distortion characteristics. This local optimization improves detection accuracy without requiring a complete redesign of the entire processing system.

The training process is segmented by region, with each classifier trained only on data relevant to its specific region. This reduces the amount of training data required for each individual classifier compared to training a single universal classifier on all possible pedestrian orientations and sizes, thereby reducing overall training time while maintaining robust detection through region-specific optimization.

Region-specific classifiers are pre-trained offline on representative data for each region. This preliminary training action allows the system to achieve robust detection performance without requiring extensive real-time processing or continuous training, as the region-specific characteristics are already captured in the pre-trained models.

The image processing system is segmented into multiple independent processing streams, each handling a specific region with its own optimized classifier. This segmentation allows each stream to process images more efficiently using region-appropriate parameters, improving overall productivity while keeping each individual processing module relatively simple and manageable.

The system uses a universal framework that can accommodate multiple region-specific classifiers. The overall architecture remains consistent and reusable across different regions, with each region simply plugging into the same processing pipeline with its own trained model. This maintains system manageability while allowing region-specific optimization.