Toward Accurate and Robust Pedestrian Detection via Variational Inference

Abstract

Pedestrian detection is notoriously considered a challenging task due to the frequent occlusion between humans. Unlike generic object detection, pedestrian detection involves a single category but dense instances, making it crucial to achieve accurate and robust object localization. By analogizing instance-level localization to a variational autoencoder and regarding the dense proposals as the latent variables, we establish a unique perspective of formulating pedestrian detection as a variational inference problem. From this vantage, we propose the Variational Pedestrian Detector (VPD), which uses a probabilistic model to estimate the true posterior of inferred proposals and applies a reparameterization trick to approximate the expected detection likelihood. In order to adapt the variational inference problem to the case of pedestrian detection, we propose a series of customized designs to cope with the issue of occlusion and spatial vibration. Specifically, we propose the Normal Gaussian and its variant of the Mixture model to parameterize the posterior in complicated scenarios. The inferred posterior is regularized by a conditional prior related to the ground-truth distribution, thus directly coupling the latent variables to specific target objects. Based on the posterior distribution, maximum detection likelihood estimation is applied to optimize the pedestrian detector, where a lightweight statistic decoder is designed to cast the detection likelihood into a parameterized form and enhance the confidence score estimation. With this variational inference process, VPD endows each proposal with the discriminative ability from its adjacent distractor due to the disentangling nature of the latent variable in variational inference, achieving accurate and robust detection in crowded scenes. Experiments conducted on CrowdHuman, CityPersons, and MS COCO demonstrate that our method is not only plug-and-play for numerous popular single-stage methods and two-stage methods but also can achieve a remarkable performance gain in highly occluded scenarios. The code for this project can be found at https://github.com/hhy-ee/VPD.