Abstract
Real-time video-surveillance systems are nowadays widespread in several applications, including public transportation. In those applications, the use of automatic video content analytics (VCA) is being increasingly adopted to support human operators in control rooms. However, VCA is only effective when its performances are such to reduce the number of false positive alarms below acceptability thresholds while still detecting events of interest. In this paper, we report the results of the evaluation of a VCA system installed on a rail transit vehicle. With respect to fixed installations, on-board ones feature specific constraints on camera installation, obstacles, environment, etc. Several VCA performance evaluation metrics have been considered, both frame-based and object-based, computed by a tool developed in Matlab. We compared the results obtained using a commercial VCA system with the ones produced by an open-source one, showing the higher performance of the former in all test conditions.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Casola, V., Esposito, M., Flammini, F., Mazzocca, N., Pragliola, C. (2013). Performance Evaluation of Video Analytics for Surveillance On-Board Trains. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_37
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DOI: https://doi.org/10.1007/978-3-319-02895-8_37
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