Vision-Based Cyclist Detection via Spatial Relationship Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional pedestrian and cyclist detection methods face challenges in accurately recognizing cyclists due to overlapping images and variations in appearance, which hampers effective on-road obstacle detection and safety.
Innovation Solution
A vision-based detection method that calculates pixel value differences and weights, utilizes textural self-similarity and spatial relationships between objects to classify features, and employs a pre-trained classifier to confirm cyclist presence by analyzing the spatial relationship between detected vehicles and pedestrians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional model comparison is used for pedestrian detection, then pedestrian recognition can be achieved through database matching, but cyclist detection fails due to image blocking and appearance variation
Solution Approach 1:
The patent applies a unified detection framework that handles both pedestrian and cyclist detection using the same spatial relationship analysis and textural self-similarity methods. The system detects human features, vehicle features, and their spatial relationships simultaneously, making the detection system versatile for multiple target types without requiring separate specialized models for each object category
Solution Approach 2:
The patent transitions from traditional single-object appearance-based detection to a multi-dimensional approach that incorporates spatial relationships between objects. By analyzing the positional, directional, and distance relationships between human features and vehicle features, the system gains an additional dimension for identification that overcomes the limitations of appearance-based methods for cyclists
2Reliability
If image-based detection is used for cyclists, then cyclist presence can be detected, but accuracy deteriorates due to mutual blocking and appearance variation
Solution Approach 1:
The patent introduces spatial relationship features as an intermediary element that mediates between the detected human features and vehicle features. Instead of directly recognizing cyclist appearance (which is obscured), the system uses the spatial configuration between the human body and vehicle as an indirect indicator, allowing reliable cyclist detection without directly observing the cyclist's full appearance
Solution Approach 2:
The patent replaces the mechanical vision-based appearance recognition system with a spatial relationship analysis system. Rather than relying on optical detection of visual features (which fails when blocked), the system substitutes with geometric and topological analysis of object positions and orientations, achieving more reliable detection under occlusion conditions
3Measurement precision
If multiple detection methods are combined for pedestrian and cyclist detection, then overall detection capability improves, but system complexity increases
Solution Approach 1:
The patent merges pedestrian detection and cyclist detection into a single unified process. Both detection tasks share common components including feature extraction, spatial relationship calculation, and classification mechanisms. The system extracts human features and vehicle features using the same methods, then applies spatial relationship analysis to both cases, reducing overall system complexity compared to separate dedicated systems
Data Source
AI summary
A vision based pedestrian and cyclist detection method includes receiving an input image, calculating a pixel value difference between each pixel and the neighbor pixels thereof, quantifying the pixel value difference as a weight of pixel, proceeding statistics for the pixel value differences and the weights, determining intersections of the statistics as a feature of the input image, classifying the feature into human feature and non-human feature, confirming the human feature belonging to cyclist according to the spatial relationship between the human feature and the detected two-wheeled vehicle, and retaining one detection result for each cyclist by suppressing other weaker spatial relationships between the human feature and the detected two-wheeled vehicle.