Citation

Wang P, Bai X. IEEE Trans. Image Process. 2019; ePub(ePub): ePub.

Copyright

(Copyright © 2019, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TIP.2019.2924171

PMID

31265395

Abstract

A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network, IPS-cGAN, is proposed for intelligent vehicular applications. The convolution backbone architecture of the generator is based on improved U-Net with residual blocks for well utilizing regional semantic information. Moreover, cross entropy loss for segmentation is introduced as the condition for generator. SandwichNet, a novel convolutional network with symmetrical input, is proposed as the discriminator for real-fake segmented images. Based on c-GAN framework, good segmentation performance could be achieved for thermal infrared pedestrians. Compared to some supervised and unsupervised segmentation algorithms, the proposed algorithm achieves higher accuracy with better robustness, especially for complex scenes.

Language: en