Abstract
Techniques to automate video surveillance around places where cameras are forbidden due to privacy concerns are yet under-addressed. This can be achieved by building conceptual models and algorithms to investigate the credibility of monitoring of events using the video frames captured by mounting the cameras so as to have the view of the entrances of such camera-forbidden areas. Evaluation of these models and algorithms require standard datasets. The proposal here is to introduce a new benchmark dataset—“EnEx dataset” as no traces specific to the problem were found in the literature. The dataset comprises of video frames captured in 5 different locations accounting 90 entry–exit event pairs based on 9 different sequences involving 36 participants. Ground statistics of the dataset is reported. This work ventures a new sub-domain for research in the area of automated video surveillance.
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Acknowledgements
The first author acknowledges Union Grants Commission, Government of India for providing financial aid to carry out this research work. The research scholars and students of the Department of studies in Computer Science, University of Mysore, Mysuru and the students of NIE Institute of Technology, Mysuru are acknowledged for their active participation in the efforts of collection of dataset and willingness to reveal their identities for publication with the consent of their respective educational institutions for research purposes only.
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Vinay Kumar, V., Nagabhushan, P., Roopa, S.N. (2020). Entry–Exit Video Surveillance: A Benchmark Dataset. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_30
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