Object Detection Based on Binocular Vision with Convolutional Neural Network
Author:
Affiliation:
1. Tongji University, School of Electronics and Information, Engineering, Shanghai, P. R. China
2. SAIC Motor Corporation Limited, Research & Advanced Technology, Department, Shanghai, P. R. China
Publisher
ACM Press
Reference23 articles.
1. Dai, J., Li, Y., He, K. and Sun, J. 2016. R-FCN: Object detection via region-based fully convolutional networks. Advances in neural information processing systems (2016), 379--387.
2. Fragkiadaki, K., Arbeláez, P., Felsen, P. and Malik, J. 2015. Learning to segment moving objects in videos. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jun. 2015), 4083--4090.
3. Lin, T. et al. 2017. Focal Loss for Dense Object Detection. international conference on computer vision. (2017), 2999--3007.
4. Girshick, R., Donahue, J., Darrell, T. and Malik, J. 2014. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition (Jun. 2014), 580--587.
5. Girshick, R. 2015. Fast R-CNN. Proceedings of the IEEE international conference on computer vision (2015), 1440--1448.
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