NDLI: CRF based method for curb detection using semantic cues and stereo depth

Content Provider	ACM Digital Library
Author	Swarup, Shanti Bhatt, Dhaivat Krishna, K Madhava Sodhi, Danish Upadhyay, Sarthak
Abstract	Curb detection is a critical component of driver assistance and autonomous driving systems. In this paper, we present a discriminative approach to the problem of curb detection under diverse road conditions. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixel-wise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450347532
DOI	10.1145/3009977.3010058
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep learning Stereovision Conditional random field Curbs
Content Type	Text
Resource Type	Article

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CRF based method for curb detection using semantic cues and stereo depth