Evaluation of Stacked Autoencoders for Pedestrian Detection

Pedestrian detection has multiple applications as video surveillance, automatic driver-assistance systems in vehicles or visual control of access. This task is challenging due to presence of factors such as poor lighting, occlusion or uncertainty in the environment. Deep learning has reached many state-of-art results in visual recognition, where one popular and simple variant is stacked autoencoders. Nonetheless, it is not clear what is the effect of each stacked autoencoders parameter in pedestrian detection performance. In this work, we propose to revise the feature representation for pedestrian detection considering the use of deep learning using stacked autoencoders with a sensitivity analysis of relevant parameters. Additionally, this paper presents a methodology for feature extraction using stacked autoencoders. The experiments show that this model is capable of creating a meaningful visual descriptor for pedestrian detection, which improves the detection performance in comparison to baseline techniques without an optimal setting of parameters. In presence of occlusion or poor people images, we found diffuse and distorted visual patterns. A future avenue is the learning of the degree of noise for improving the generalization capabilities of the learned features