Object Detection and Viewpoint Estimation with Auto-Masking Neural Network

Abstract

Simultaneously detecting an object and determining its pose has become a popular research topic in recent years. Due to the large variances of the object appearance in images, it is critical to capture the discriminative object parts that can provide key information about the object pose. Recent part-based models have obtained state-of-the-art results for this task. However, such models either require manually defined object parts with heavy supervision or a complicated algorithm to find discriminative object parts. In this study, we have designed a novel deep architecture, called Auto-masking Neural Network (ANN), for object detection and viewpoint estimation. ANN can automatically learn to select the most discriminative object parts across different viewpoints from training images. We also propose a method of accurate continuous viewpoint estimation based on the output of ANN. Experimental results on related datasets show that ANN outperforms previous methods.