  We aim to tackle a novel task in action detection - Online Detection of
Action Start (ODAS) in untrimmed, streaming videos. The goal of ODAS is to
detect the start of an action instance, with high categorization accuracy and
low detection latency. ODAS is important in many applications such as early
alert generation to allow timely security or emergency response. We propose
three novel methods to specifically address the challenges in training ODAS
models: (1) hard negative samples generation based on Generative Adversarial
Network (GAN) to distinguish ambiguous background, (2) explicitly modeling the
temporal consistency between data around action start and data succeeding
action start, and (3) adaptive sampling strategy to handle the scarcity of
training data. We conduct extensive experiments using THUMOS'14 and
ActivityNet. We show that our proposed methods lead to significant performance
gains and improve the state-of-the-art methods. An ablation study confirms the
effectiveness of each proposed method.
