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Proceedings Paper

Recurrent neural network based virtual detection line
Author(s): Roberts Kadikis
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Paper Abstract

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

Paper Details

Date Published: 13 April 2018
PDF: 9 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961V (13 April 2018); doi: 10.1117/12.2309772
Show Author Affiliations
Roberts Kadikis, Institute of Electronics and Computer Science (Latvia)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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