  Deep learning-based computer vision is usually data-hungry. Many researchers
attempt to augment datasets with synthesized data to improve model robustness.
However, the augmentation of popular pedestrian datasets, such as Caltech and
Citypersons, can be extremely challenging because real pedestrians are commonly
in low quality. Due to the factors like occlusions, blurs, and low-resolution,
it is significantly difficult for existing augmentation approaches, which
generally synthesize data using 3D engines or generative adversarial networks
(GANs), to generate realistic-looking pedestrians. Alternatively, to access
much more natural-looking pedestrians, we propose to augment pedestrian
detection datasets by transforming real pedestrians from the same dataset into
different shapes. Accordingly, we propose the Shape Transformation-based
Dataset Augmentation (STDA) framework. The proposed framework is composed of
two subsequent modules, i.e. the shape-guided deformation and the environment
adaptation. In the first module, we introduce a shape-guided warping field to
help deform the shape of a real pedestrian into a different shape. Then, in the
second stage, we propose an environment-aware blending map to better adapt the
deformed pedestrians into surrounding environments, obtaining more
realistic-looking pedestrians and more beneficial augmentation results for
pedestrian detection. Extensive empirical studies on different pedestrian
detection benchmarks show that the proposed STDA framework consistently
produces much better augmentation results than other pedestrian synthesis
approaches using low-quality pedestrians. By augmenting the original datasets,
our proposed framework also improves the baseline pedestrian detector by up to
38% on the evaluated benchmarks, achieving state-of-the-art performance.
