Cross-Modality Proposal-Guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection

RGB-Thermal (RGB-T) pedestrian detection aims to locate pedestrians in RGB-T image pairs to exploit the complementation between the two modalities for improving detection robustness in extreme conditions. Most existing algorithms assume that the RGB-T image pairs are well registered, while in the real world, they are not ideally aligned due to parallax or different field-of-view of the cameras. The pedestrians in misaligned image pairs may be located at different positions in two images, which results in two challenges: 1) how to achieve inter-modality complementation using spatially misaligned RGB-T pedestrian patches and 2) how to recognize unpaired pedestrians at the boundary. To address these issues, we propose a new paradigm for unregistered RGB-T pedestrian detection, which predicts two separate pedestrian locations in RGB and thermal images. Specifically, we propose a cross-modality proposal-guided feature mining (CPFM) mechanism to extract two precise fusion features for representing a pedestrian in the two modalities, even if the given RGB-T image pair is unaligned. It enables us to effectively exploit the complementation between the two modalities. With the CPFM mechanism, we build a two-stream dense detector that predicts two pedestrian locations in the two modalities based on the corresponding fusion features mined by the CPFM mechanism. In addition, we design a data augmentation method, named Homography, to simulate the discrepancy in scales and views between images. We also investigate two non-maximum suppression (NMS) methods for post-processing purposes. Favorable experimental results demonstrate the effectiveness and robustness of our method in addressing unregistered pedestrians with different shifts.