Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks
This addresses the problem of limited labeled data for pedestrian detection in smart cities, but it is incremental as it combines existing methods (DCGAN and SSD) for data augmentation.
The paper tackles pedestrian detection in the wild by using Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) to generate augmented data, resulting in a substantial improvement in detection results compared to SSD alone.
In this work, we examine the feasibility of applying Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to handle with the challenge of pedestrian detection in the wild. Specifically, we attempted to use in-fill completion (where a portion of the image is masked) to generate random transformations of images with portions missing to expand existing labelled datasets. In our work, GAN has been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities. The object detector experiment performed by training GAN model along with SSD provided a substantial improvement in the results. This approach presents a very interesting overview in the current state of art on GAN networks for object detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech, KITTI data set for training and testing the network under different resolutions and the experimental results with comparison been showedbetween DCGAN cascaded with SSD and SSD itself.