Abstract
We propose a novel method for extracting target objects such as pedestrians from a given rough initial region of infrared video images from automobiles. The detection of pedestrians from cars is imperative for predicting their subsequent movements in safe driving assistance. However, the automatic extraction of pedestrian regions with various clothing styles and postures is difficult due to the complexities of textural/brightness patterns in clothing and against background scenes. We approach this problem by introducing an object extraction method that reveals the object boundaries while assimilating the variation of brightness distribution. The proposed method can correctly extract the object area by introducing a brightness histogram as a probability density function and an object shape distribution map as a priori probability for the preprocess of the kernel density estimator. We first confirm that the accuracy and computation speed in general cases are improved in comparison to those of the original extractor. We also extend the object distribution map to reflect the shapes of pedestrians with various postures in real situations. Preliminary experimental results are given to show the potential of our proposed method for extracting pedestrians in various clothing, postures, and backgrounds.
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Mase, K., Imaeda, K., Shiraki, N., Watanabe, A. (2009). Extraction of Pedestrian Regions Using Histogram and Locally Estimated Feature Distribution. In: Takeda, K., Erdogan, H., Hansen, J.H.L., Abut, H. (eds) In-Vehicle Corpus and Signal Processing for Driver Behavior. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79582-9_9
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DOI: https://doi.org/10.1007/978-0-387-79582-9_9
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