Online Detection of Action Start in Untrimmed, Streaming Videos
It addresses a novel task for applications like early alert generation in security or emergency response, but the methods are incremental improvements on existing approaches.
The paper tackles the problem of detecting the start of actions in untrimmed, streaming videos, aiming for high accuracy and low latency, and shows significant performance gains on THUMOS'14 and ActivityNet datasets.
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.