Pedestrian Detection System in Low Illumination Conditions through Data Fusion of Image and Range Sensor
Date Issued
2014
Date
2014
Author(s)
Huang, Pang-Ting
Abstract
The development of pedestrian detection techniques mainly focused on the research on finding suitable visual feature in the past decades. However, illumination is not only important to enable human visual ability to view the surroundings, but is also very critical to the choice of visual features in the vision-based detection methods. Since the visual information can be greatly affected by different illuminations, the resulting detection methods can produce unreliable results. To cope with such difficult situation due to uncontrolled lighting conditions, an Image-Range Fusion System (IRFS) is proposed by applying the image data from a camera and the range data from a range sensor simultaneously. For the image part, a Dynamically Illuminated Object (DIO) detector is proposed to overcome the possible problem caused by the uncertain partial lighting condition within a low-illumination environment. Specifically, the DIO detector applies two kinds of features including the Histograms of Oriented Gradients (HOG) for representing the shape information and the Logarithm Weighted Pattern (LWP) for the textural information. Note that LWP gives an 86% recall rate at 〖10〗^(-4) false positive per window, and range data are sampled under a probabilistic model to reinforce the performance by the precise locating ability of the range sensor. To validate our results, several experiments have been conducted, and the overall system performance is shown to be 92.51% / 88.92% of recall/ precision under real-time computing setting.
Subjects
行人偵測
低照度
距離資訊
感知融合
Type
thesis
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