Recently, as autonomous cars are developing very fast, it is the most crucial task to detect pedestrians for autonomous driving. Convolution neural network based on pedestrian detection models has gained enormous success in many applications. However, these models need a large amount of annotated and labeled datasets for training process which requires lots of time and human effort. For training samples, the diversity and quantity of datasets are very important. The proposed framework is based on Deep Convolutional Generative Adversarial Networks (DCGAN), able to generate realistic pedestrians. Experimental results show that DCGAN framework is able to synthesize real pedestrian images with diversity. The synthesized samples can be included in training data to improve the performance of pedestrian detectors. 24,770 images including PETA dataset, Inria dataset were used for the training process.
Generating pedestrian training dataset using DCGAN / Kim, D. D.; Shahid, M. T.; Kim, Y.; Lee, W. J.; Song, H. C.; Piccialli, F.; Choi, K. N.. - (2019), pp. 1-4. (Intervento presentato al convegno 3rd International Conference on Advances in Image Processing, ICAIP 2019 tenutosi a chn nel 2019) [10.1145/3373419.3373458].
Generating pedestrian training dataset using DCGAN
Piccialli F.
;
2019
Abstract
Recently, as autonomous cars are developing very fast, it is the most crucial task to detect pedestrians for autonomous driving. Convolution neural network based on pedestrian detection models has gained enormous success in many applications. However, these models need a large amount of annotated and labeled datasets for training process which requires lots of time and human effort. For training samples, the diversity and quantity of datasets are very important. The proposed framework is based on Deep Convolutional Generative Adversarial Networks (DCGAN), able to generate realistic pedestrians. Experimental results show that DCGAN framework is able to synthesize real pedestrian images with diversity. The synthesized samples can be included in training data to improve the performance of pedestrian detectors. 24,770 images including PETA dataset, Inria dataset were used for the training process.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
