Variational Pedestrian Detection

TL;DR

This paper introduces a novel variational inference approach to pedestrian detection, addressing occlusion and dense crowds by reformulating detection as a latent variable optimization problem, improving accuracy and efficiency.

Contribution

It proposes a new variational inference framework with a customized Auto Encoding Variational Bayes algorithm for pedestrian detection, enhancing existing detectors' performance.

Findings

Improved detection accuracy on CrowdHuman and CityPersons datasets.

Effective handling of dense pedestrian scenarios with occlusions.

Versatile application to both single-stage and two-stage detectors.

Abstract

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.