Deep Active Perception for Object Detection using Navigation Proposals
Stefanos Ginargiros Nikolaos Passalis Anastasios Tefas
Deep Learning (DL) has brought significant advances to robotics vision tasks.
However, most existing DL methods have a major shortcoming, they rely on a
static inference paradigm inherent in traditional computer vision pipelines. On
the other hand, recent studies have found that active perception improves the
perception abilities of various models by going beyond these static paradigms.
Despite the significant potential of active perception, it poses several
challenges, primarily involving significant changes in training pipelines for
deep learning models. To overcome these limitations, in this work, we propose a
generic supervised active perception pipeline for object detection that can be
trained using existing off-the-shelf object detectors, while also leveraging
advances in simulation environments. To this end, the proposed method employs
an additional neural network architecture that estimates better viewpoints in
cases where the object detector confidence is insufficient. The proposed method
was evaluated on synthetic datasets, constructed within the Webots robotics
simulator, showcasing its effectiveness in two object detection cases.