Título:
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Online detection of action start in untrimmed, streaming videos
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Autor/a:
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Shou, Zheng; Pan, Junting; Chan, Jonathan; Miyazawa, Kazuyuki; Mansour, Hassan; Vetro, Anthony; Giró Nieto, Xavier; Chang, Shih-Fu
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
Abstract:
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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 |
Abstract:
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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. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo -Àrees temàtiques de la UPC::So, imatge i multimèdia::Creació multimèdia::Vídeo digital -Streaming video -Digital video -Internet videos -Transmissió de vídeo -Vídeo digital -Vídeos per Internet |
Derechos:
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Tipo de documento:
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Artículo - Versión presentada Objeto de conferencia |
Editor:
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Springer
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