  Object detection is the key technique to a number of Computer Vision
applications, but it often requires large amounts of annotated data to achieve
decent results. Moreover, for pedestrian detection specifically, the collected
data might contain some personally identifiable information (PII), which is
highly restricted in many countries. This label intensive and privacy
concerning task has recently led to an increasing interest in training the
detection models using synthetically generated pedestrian datasets collected
with a photo-realistic video game engine. The engine is able to generate
unlimited amounts of data with precise and consistent annotations, which gives
potential for significant gains in the real-world applications. However, the
use of synthetic data for training introduces a synthetic-to-real domain shift
aggravating the final performance. To close the gap between the real and
synthetic data, we propose to use a Generative Adversarial Network (GAN), which
performsparameterized unpaired image-to-image translation to generate more
realistic images. The key benefit of using the GAN is its intrinsic preference
of low-level changes to geometric ones, which means annotations of a given
synthetic image remain accurate even after domain translation is performed thus
eliminating the need for labeling real data. We extensively experimented with
the proposed method using MOTSynth dataset to train and MOT17 and MOT20
detection datasets to test, with experimental results demonstrating the
effectiveness of this method. Our approach not only produces visually plausible
samples but also does not require any labels of the real domain thus making it
applicable to the variety of downstream tasks.
