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Object detection is the key technique to a number of Computer Visionapplications, but it often requires large amounts of annotated data to achievedecent results. Moreover, for pedestrian detection specifically, the collecteddata might contain some personally identifiable information (PII), which ishighly restricted in many countries. This label intensive and privacyconcerning task has recently led to an increasing interest in training thedetection models using synthetically generated pedestrian datasets collectedwith a photo-realistic video game engine. The engine is able to generateunlimited amounts of data with precise and consistent annotations, which givespotential for significant gains in the real-world applications. However, theuse of synthetic data for training introduces a synthetic-to-real domain shiftaggravating the final performance. To close the gap between the real andsynthetic data, we propose to use a Generative Adversarial Network (GAN), whichperformsparameterized unpaired image-to-image translation to generate morerealistic images. The key benefit of using the GAN is its intrinsic preferenceof low-level changes to geometric ones, which means annotations of a givensynthetic image remain accurate even after domain translation is performed thuseliminating the need for labeling real data. We extensively experimented withthe proposed method using MOTSynth dataset to train and MOT17 and MOT20detection datasets to test, with experimental results demonstrating theeffectiveness of this method. Our approach not only produces visually plausiblesamples but also does not require any labels of the real domain thus making itapplicable to the variety of downstream tasks.

ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection