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This article is cited in 3 scientific papers (total in 3 papers)
IMAGE PROCESSING, PATTERN RECOGNITION
A framework of reading timestamps for surveillance video
J. Cheng, W. Dai Hubei Polytechnic University, Huangshi 435003, Hubei, China
Abstract:
This paper presents a framework to automatically read timestamps for surveillance video. Reading timestamps from surveillance video is difficult due to the challenges such as color variety, font diversity, noise, and low resolution. The proposed algorithm overcomes these challenges by using the deep learning framework. The framework has included: training of both timestamp localization and recognition in a single end-to-end pass, the structure of the recognition CNN and the geometry of its input layer that preserves the aspect of the timestamps and adapts its resolution to the data. The proposed method achieves state-of-the-art accuracy in the end-to-end timestamps recognition on our datasets, whilst being an order of magnitude faster than competing methods. The framework can be improved the market competitiveness of panoramic video surveillance products.
Keywords:
surveillance video, timestamp localization, timestamp recognition.
Received: 25.05.2018 Accepted: 10.10.2018
Citation:
J. Cheng, W. Dai, “A framework of reading timestamps for surveillance video”, Computer Optics, 43:1 (2019), 72–77
Linking options:
https://www.mathnet.ru/eng/co605 https://www.mathnet.ru/eng/co/v43/i1/p72
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Abstract page: | 130 | Full-text PDF : | 191 | References: | 19 |
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