Study on the Application of EfficientDet to Real-Time Classification of Infrared Images from Video Surveillance

Mendes, Filipe; Fernades, Armando M.; Fernandes, Luis; Piedade, Fernando; Chaves, Paulo

http://hdl.handle.net/10400.26/58686

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Abstract(s)

The monitoring of certain areas can be a tedious and non-interactive task that usually leads to some missed occurrences. With the development of object-detection models, this problem can be mitigated and reduced. In this paper, a rare application of EfficientDet model to the analysis of footage from an infrared camera in real-time is studied. The model will be used to detect deers, people and cars in images captured at the surroundings of a classified facility from where we do not yet have images available. Consequently, we show the process of creating the model and discuss the problems raised by the real-time performance limitations as well as by the available data limitations. The results obtained suggest that this kind of implementation has the potential to improve existing surveillance systems.

Keywords

Object-Detection AI EfficientDet Infrared Thermal Deers

URI

http://hdl.handle.net/10400.26/58686

Citation

F. Mendes, A. M. Fernandes, L. Fernandes, F. Piedade and P. Chaves, "Study on the Application of EfficientDet to Real-Time Classification of Infrared Images from Video Surveillance," 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), Prague, Czech Republic, 2022, pp. 1-6