Cross Datasets Vegetation Detection With Spatial Prior And Local Context
2016 IEEE Intelligent Vehicles Symposium (IV)(2016)
摘要
In this paper, we propose a vision-based approach for roadside vegetation detection by superpixel matching with local context. Unlike previous detection methods which seek help from additional sensors such as lidar, our algorithm only requires an off-the-shelf camera. The proposed method contains two stages. In the first stage, a superpixel database is constructed by segmenting training images into superpixels, and each superpixel patch is represented with multiple features. After that, the appearance information of vegetation or non-vegetation is encoded in the superpixel database. In the second stage, vegetation detection in each testing image is achieved by superpixel matching. The test image is segmented into superpixels and the (vegetation) label cost of each superpixel is derived by comparing with the k-nearest neighbors in the superpixel database. Furthermore, we incorporate the local context information through the feedback to refine superpixel matching. Taking this context information into account, Markov Random Field (MRF) is utilized to further improve the classification accuracy. Besides, considering the stable layout of road scene images, we utilize spatial priors of road scene to guide vegetation classification. Experiments on real-world datasets demonstrate the promise of our method.
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关键词
cross dataset vegetation detection,vision-based approach,roadside vegetation detection,superpixel matching,sensors,lidar,off-the-shelf camera,superpixel database,training image segmentation,superpixel patch representation,testing image,k-nearest neighbors,local context information,Markov random field,MRF,road scene image layout,spatial priors,vegetation classification
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