Title: Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory

Authors: Jung Uk Kim, Sungjune Park, and Yong Man Ro

Recently, automated surveillance cameras can change a visible sensor and a thermal sensor for all-day operation. However, existing single-modal pedestrian detectors mainly focus on detecting pedestrians in only one specific modality (i.e., visible or thermal), so they cannot effectively cope with other modal inputs. In addition, recent multispectral pedestrian detectors have shown remarkable performance by adopting multispectral modalities, but they also have limitations in practical applications (e.g., different Field-of-View (FoV) and frame rate). In this paper, we introduce a versatile pedestrian detector that shows robust detection performance in any single modality. We propose a multisensory-matching contrastive loss to reduce the difference between the visual representation of pedestrians in the visible and the thermal modalities. Moreover, to make the proposed method perform robust detection on a single modality, we design a Multispectral Recalling (MSR) Memory. The MSR Memory enhances the visual representation of the single modal features by recalling that of the multispectral modalities. To guide the MSR Memory to store the contexts of the multispectral modalities, we introduce a multispectral recalling loss. It enables the pedestrian detector to encode more discriminative features in a single input modality. We would like to insist that our method is a step forward detector that can be applied to a variety of real-world applications. The comprehensive experimental results verify the effectiveness of the proposed method.
 

IMAGE VIDEO SYSTEM (IVY.) KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY (KAIST), AAAI 2022