Pedestrian Detection Using Depth and Image Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional pedestrian detecting systems face challenges in accurately identifying pedestrians in complex, insufficiently-illuminated environments due to high contrast and depth complexities, as they rely on appearance features alone and struggle with processing environments with varying luminance intensities.
Innovation Solution
A pedestrian detecting system incorporating a depth capturing unit, an image capturing unit, and a composite processing unit that performs weighted scoring on spatial and appearance confidence values, utilizing a dynamically illuminated object detector to enhance accuracy in low-luminance conditions by obtaining ideal-illuminated image regions and combining depth and image feature information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a depth sensor is used to detect distance, then the distance between pedestrian and vehicle can be determined, but the appearance of the pedestrian cannot be detected
Solution Approach 1:
The patent combines depth sensor data with image sensor data into a unified detection framework. The composite processing unit integrates spatial information from the depth sensor with appearance features from the image sensor, allowing simultaneous acquisition of both distance and visual characteristics through data fusion
2Productivity
If conventional pedestrian detection methods are used in high contrast environments, then detection speed is maintained, but detection accuracy deteriorates due to high contrast and insufficient illumination
Solution Approach 1:
The patent introduces depth information as an additional dimension beyond traditional two-dimensional image analysis. By incorporating the third dimension (depth/spatial information) from the depth sensor, the system creates a three-dimensional understanding of the scene, enabling accurate pedestrian detection in high contrast environments where traditional 2D methods fail
3Device complexity
If the training model focuses on the entire image, then processing is simplified, but recognition accuracy decreases for target objects with partial ideal image regions
Solution Approach 1:
The patent segments the detection process into distinct components: depth information processing, image feature extraction, and composite scoring. By dividing the overall detection task into separate processing streams that are then integrated, the system achieves both manageable complexity and high accuracy for partial object recognition
Data Source
AI summary
A pedestrian detecting system includes a depth capturing unit, an image capturing unit and a composite processing unit. The depth capturing unit is configured to detect and obtain spatial information of a target object. The image capturing unit is configured to capture an image of the target object and recognize the image, thereby obtaining image feature information of the target object. The composite processing unit is electrically connected to the depth capturing unit and the image capturing unit, wherein the composite processing unit is configured to receive the spatial information and the image feature information and to perform a scoring scheme to detect and determine if the target object is a pedestrian. The scoring scheme performs weighted scoring on a spatial confidence and an appearance confidence to obtain a composite scoring value to determine if the target object is the pedestrian.