Gaussian Processes with Context-Supported Priors for Active Object Localization

TL;DR

This paper introduces a Bayesian optimization approach using contextual visual data and Gaussian Processes to improve the accuracy and efficiency of object localization in images, addressing limitations of current methods.

Contribution

The paper presents a novel active search algorithm combining CNN-based offset estimation with Gaussian Processes for principled object localization.

Findings

Significant improvement over baseline bounding-box regression in pedestrian localization

Efficient active search reduces localization error

Method provides a more interpretable system for high-level vision tasks

Abstract

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. In experiments, we compare our approach to a state-of-theart bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.