Abstract. 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 specifi-
cally 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 signifi-
cant performance gains and improve the state-of-the-art methods. An
ablation study confirms the effectiveness of each proposed method