STADe: Sensory Temporal Action Detection via Temporal-Spectral Representation Learning.

Abstract

Temporal action detection (TAD) is a vital challenge in computer vision and the Internet of Things, aiming to detect and identify actions within temporal sequences. While TAD has primarily been associated with video data, its applications can also be extended to sensor data, opening up opportunities for various real-world applications. However, applying existing TAD models to sensory signals presents distinct challenges such as varying sampling rates, intricate pattern structures, and subtle, noise-prone patterns. In response to these challenges, we propose a Sensory Temporal Action Detection (STADe) model. STADe leverages Fourier kernels and adaptive frequency filtering to adaptively capture the nuanced interplay of temporal and frequency features underlying complex patterns. Moreover, STADe embraces adaptability by employing deep fusion at varying resolutions and scales, making it versatile enough to accommodate diverse data characteristics, such as the wide spectrum of sampling rates and action durations encountered in sensory signals. Unlike conventional models with unidirectional category-to-proposal dependencies, STADe adopts a cross-cascade predictor to introduce bidirectional and temporal dependencies within categories. To extensively evaluate STADe and promote future research in sensory TAD, we establish three diverse datasets using various sensors, featuring diverse sensor types, action categories, and sampling rates. Experiments across one public and our three new datasets demonstrate STADe's superior performance over state-of-the-art TAD models in sensory TAD tasks.