Performance Boost of Attribute-aware Semantic Segmentation via Data Augmentation for Driver Assistance

This paper is an extension of our work in developing an attribute-aware semantic segmentation method which focuses on pedestrian understanding in a traffic scene. Recently, the trending topic of semantic segmentation has been expanded to be able to collaborate with the object’s attributes recognition task; Here, it refers to recognizing a pedestrian’s body orientation. The attribute-aware semantic segmentation can be more beneficial for driver assistance compared to the conventional semantic segmentation because it can provide a more informative output to the system. In this paper, we conduct a study of the data augmentation usage as an effort to enhance the performance of the attribute-aware semantic segmentation task. The experiments show that the proposed method in augmenting the training data is able to improve the model’s performance. We also demonstrate some of qualitative results and discuss the benefits to a driver assistance system.

[1]  Antonio M. López,et al.  The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  M. D. Sulistiyo,et al.  Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations , 2020, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[3]  Xiaojuan Qi,et al.  ICNet for Real-Time Semantic Segmentation on High-Resolution Images , 2017, ECCV.

[4]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[6]  Haiqiang Chen,et al.  Road segmentation for all-day outdoor robot navigation , 2018, Neurocomputing.

[7]  Shau-Shiun Jan,et al.  Combination of computer vision detection and segmentation for autonomous driving , 2018, 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[8]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Linlin Liu,et al.  ClothingOut: a category-supervised GAN model for clothing segmentation and retrieval , 2018, Neural Computing and Applications.

[10]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  M. Jorge Cardoso,et al.  Improving Data Augmentation for Medical Image Segmentation , 2018 .

[12]  Carsten Rother,et al.  Panoptic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[14]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Pin Tao,et al.  Optimizing Data Augmentation for Semantic Segmentation on Small-Scale Dataset , 2019, Proceedings of the 2nd International Conference on Control and Computer Vision - ICCCV 2019.

[16]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[17]  Cyrill Stachniss,et al.  Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Hiroshi Murase,et al.  A Preliminary Study of Attribute-aware Semantic Segmentation for Pedestrian Understanding , 2017 .

[19]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yu Cheng,et al.  Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing , 2018, ACM Multimedia.

[21]  Tadahiro Taniguchi,et al.  Contextual scene segmentation of driving behavior based on double articulation analyzer , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Weichao Xu,et al.  Real-time object detection and semantic segmentation for autonomous driving , 2018, International Symposium on Multispectral Image Processing and Pattern Recognition.

[23]  Linhui Li,et al.  Traffic Scene Segmentation Based on RGB-D Image and Deep Learning , 2018, IEEE Transactions on Intelligent Transportation Systems.

[24]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[26]  Roberto Cipolla,et al.  Semantic object classes in video: A high-definition ground truth database , 2009, Pattern Recognit. Lett..

[27]  Seungmin Rho,et al.  Medical image semantic segmentation based on deep learning , 2017, Neural Computing and Applications.

[28]  J.M. Alvarez,et al.  Illuminant-invariant model-based road segmentation , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[29]  Daisuke Deguchi,et al.  Attribute-aware Semantic Segmentation of Road Scenes for Understanding Pedestrian Orientations , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).