Multimodal Human Detection Using YOLO and Representation Learning for Robot Perception

This work concentrates on the problem of multisensor people detection using YOLO trained on four distinct modalities: depth and intensity LiDAR-maps, RGB, and ‘thermal’ images. RGB cameras, ubiquitous in this application domain, offer great resolution but struggle with adverse lighting conditions resulting in overexposed or underexposed images which then impact negatively on the performance of the algorithms. Thermal (long-wave infrared) cameras are more resilient against varying light conditions and provide complementary textural features, although with lower resolution when compared to RGB cameras. LiDAR sensors, while having a significantly low resolution, contribute to a physically interpretable mapping of the environment providing precise information regarding size/dimension and location of the objects. The main goal of this work is to tackle people detection using deep-models trained on single and multi-modality representations. To support the experimental part this work introduces a new multimodal dataset (called MID-3K). MID-3K allows the development of data fusion strategies by leveraging four modalities (obtained from three distinct exteroceptive sensors mounted on a mobile robot). Leveraging on a single-modality YOLO framework, we propose a multimodal representation learning approach to improve the baseline performance and to capture more relevant features across all input modalities. The evaluation of the proposed detection pipeline is conducted on the MID-3K dataset, where the reported results are grounded on state-of-the-art performance measures. The new dataset is available in a GitHub repository^{1 1}MID-3K dataset: https://kennedyk1.github.io/MID-3K/.