A Study of Dimensionality Reduction Impact on an Approach to People Detection in Gigapixel Images

Abstract

Digital images are found in several sizes and are easily displayed on a computer screen using techniques that can reduce their dimensions. Moreover, algorithms are used to process images to perform several tasks, for instance, detection of people. Recently, gigapixel images emerged, providing a huge amount of data; however, algorithms for people detection have been usually tested only on regular size images. This paper presents an impact analysis of the resolution reduction in the detection of people in gigapixel images. People detectors were trained with the INRIA and CALTECH data sets and results show that, although gigapixel images provide a huge false positive rate, the resolution reduction significantly decreases the number of bounding boxes and false positives, however, increasing the rate of missing people.