Closing the Gap in Human Behavior Analysis: A Pipeline for Synthesizing Trimodal Data
This work addresses a data gap for researchers in human behavior analysis, though it is incremental as it builds on existing image translation methods.
The research tackled the shortage of trimodal datasets for Human Behavior Analysis by introducing a generative technique to synthesize RGB, thermal, and depth data from existing RGB images, enabling training in data-limited, low-light, or privacy-sensitive settings.
In pervasive machine learning, especially in Human Behavior Analysis (HBA), RGB has been the primary modality due to its accessibility and richness of information. However, linked with its benefits are challenges, including sensitivity to lighting conditions and privacy concerns. One possibility to overcome these vulnerabilities is to resort to different modalities. For instance, thermal is particularly adept at accentuating human forms, while depth adds crucial contextual layers. Despite their known benefits, only a few HBA-specific datasets that integrate these modalities exist. To address this shortage, our research introduces a novel generative technique for creating trimodal, i.e., RGB, thermal, and depth, human-focused datasets. This technique capitalizes on human segmentation masks derived from RGB images, combined with thermal and depth backgrounds that are sourced automatically. With these two ingredients, we synthesize depth and thermal counterparts from existing RGB data utilizing conditional image-to-image translation. By employing this approach, we generate trimodal data that can be leveraged to train models for settings with limited data, bad lightning conditions, or privacy-sensitive areas.