Authors: Yonglin Tian,Xuan Li,Kunfeng Wang,Fei-Yue Wang
ArXiv: 1712.08470
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Abstract URL: http://arxiv.org/abs/1712.08470v1
In the area of computer vision, deep learning has produced a variety of
state-of-the-art models that rely on massive labeled data. However, collecting
and annotating images from the real world has a great demand for labor and
money investments and is usually too passive to build datasets with specific
characteristics, such as small area of objects and high occlusion level. Under
the framework of Parallel Vision, this paper presents a purposeful way to
design artificial scenes and automatically generate virtual images with precise
annotations. A virtual dataset named ParallelEye is built, which can be used
for several computer vision tasks. Then, by training the DPM (Deformable Parts
Model) and Faster R-CNN detectors, we prove that the performance of models can
be significantly improved by combining ParallelEye with publicly available
real-world datasets during the training phase. In addition, we investigate the
potential of testing the trained models from a specific aspect using
intentionally designed virtual datasets, in order to discover the flaws of
trained models. From the experimental results, we conclude that our virtual
dataset is viable to train and test the object detectors.