Adaptive motion enhancement for passive non-line-of-sight action recognition

Most current recognition methods for human action are designed for line-of-sight (LOS) scenarios, where the targets are assumed to be directly visible. However, in numerous action recognition applications, we often encounter non-line-of-sight (NLOS) situations, e.g., rescue operations, security, and autonomous vehicles. In this case, it becomes imperative that we develop methods that can effectively identify human actions outside the line of sight, such as those behind obstacles or around corners. It is also important to note that most existing NLOS approaches rely on expensive active imaging equipment, which hinders practical deployment. To address these challenges, we propose AME-Net (Adaptive Motion Enhancement Network), a novel passive NLOS action recognition framework that identifies human actions by analyzing reflections on visible relay walls using only standard RGB cameras. AME-Net adaptively amplifies subtle motion cues and mitigates environmental variability, enabling accurate and robust recognition in NLOS conditions. Furthermore, we introduce NLOS-Action, the first dataset specifically designed for passive NLOS action recognition, containing both synthetic and real-world sequences. Pursuant to our extensive experiments based on the dataset, this paper convincingly demonstrates effectiveness and practicality of the proposed AME-Net for NLOS action recognitions.