@MASTERSTHESIS{ 2022:597497,
 	title = {An efficient model for identifying firearm threats in videos},
 	year = {2022},
 	url = "https://tede2.pucrs.br/tede2/handle/tede/10593",
 	abstract = "For a society to prosper, its members must feel safe in their everyday lives; otherwise, fear would start to take over the population, causing stress and panic and, consequently, reducing the quality of life. Several policies and measures are usually adopted to preserve people?s security, but as the population grows and firearms become more accessible, society?s security becomes more threatened. Concerned with this, several works sought to explore the use of security cameras, one of the most commonly used security measures, and identify when a threatening event occurs. However, these works do not have common comparison practices, standard datasets, or constraints for the datasets used. The main goal of this work is to explore methods and strategies to address the challenge of firearm threat detection while assuming a scenario of a surveillance system with limited hardware. To achieve this goal, we sought well-known efficient neural networks from the state-of-the-art and model-compression techniques to have a solid basis to start from and well-developed strategies that could further improve their performance. We also propose a new challenging dataset for identifying firearm threats that follows rigorous controls to ensure the quality of the data used. To the best of our best knowledge, ours is the largest dataset available in the area based on frame-level annotations and that uses only real-world data. Our dataset is available online, alongside the tools used to create it, making it easier to expand it further. Moreover, we evaluated the performance of some state-of-the-art methods on it, and the obtained results corroborate with its difficulty. We provide an extensive set of experiments to present clearly each approach?s strengths and weaknesses and their impact on the detection performance. We also conducted experiments on different environments to evaluate how these approaches performed on different hardware conditions. We also clarified which ones are most advantageous or are more versatile and work well on our scenarios.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Escola Polit?cnica}
}