Semantic video segmentation for autonomous driving
Minh Triet Chau
TL;DR
This paper improves real-time semantic video segmentation for autonomous driving by reducing computational speed without sacrificing accuracy, tested on the KITTI dataset.
Contribution
It demonstrates that the speed of fully convolutional networks can be halved while maintaining accuracy in real-time road detection.
Findings
Speed can be halved without losing accuracy
Effective on KITTI dataset
Enhances real-time autonomous driving systems
Abstract
We aim to solve semantic video segmentation in autonomous driving, namely road detection in real time video, using techniques discussed in (Shelhamer et al., 2016a). While fully convolutional network gives good result, we show that the speed can be halved while preserving the accuracy. The test dataset being used is KITTI, which consists of real footage from Germany's streets.
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Taxonomy
TopicsAdvanced Neural Network Applications · Medical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques