Online detection of action start in untrimmed, streaming videos
Visualitza/Obre
10.1007/978-3-030-01219-9_33
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/123701
Tipus de documentComunicació de congrés
Data publicació2018
EditorSpringer
Condicions d'accésAccés obert
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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 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.
Descripció
This is a post-peer-review, pre-copyedit version of an article published in: Lecture Notes in Computer Sciences, vol. 11207. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-01219-9_33
CitacióShou, Z., Pan, J., Chan, J., Miyazawa, K., Mansour, H., Vetro, A., Giro, X., Chang, S. Online detection of action start in untrimmed, streaming videos. A: European Conference on Computer Vision. "Computer Vision – ECCV 2018. 15th European Conference, Munich, Germany, September 8-14, 2018, proceedings, part I". Berlín: Springer, 2018, p. 551-568.
Versió de l'editorhttp://openaccess.thecvf.com/content_ECCV_2018/html/Zheng_Shou_Online_Detection_of_ECCV_2018_paper.html
Altres identificadorshttps://arxiv.org/abs/1802.06822
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Zheng_Shou_Online_Detection_of_ECCV_2018_paper.pdf | PONÈNCIA | 450,0Kb | Visualitza/Obre |