Detecting Nonexistent Pedestrians

Jui-Ting Chien, Chia-Jung Chou, Ding-Jie Chen, Hwann-Tzong Chen

Abstract: We explore beyond object detection and semantic segmentation, and propose to address the problem of estimating the presence probabilities of nonexistent pedestrians in a street scene. Our method builds upon a combination of generative and discriminative procedures to achieve the perceptual capability of figuring out missing visual information. We adopt state-of-the-art inpainting techniques to generate the training data for nonexistent pedestrian detection. The learned detector can predict the probability of observing a pedestrian at some location in image, even if that location exhibits only the background. We evaluate our method by inserting pedestrians into images according to the presence probabilities and conducting user study to determine the 'realisticness' of synthetic images. The empirical results show that our method can capture the idea of where the reasonable places are for pedestrians to walk or stand in a street scene.