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Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms
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Author(s): Nancy E. Ochoa Guevara (Fundación Universitaria Panamericana, Colombia), Andres Esteban Puerto Lara (Fundación Universitaria Panamericana, Colombia), Nelson F. Rosas Jimenez (Fundación Universitaria Panamericana, Colombia), Wilmar Calderón Torres (Fundación Universitaria Panamericana, Colombia), Laura M. Grisales García (Fundación Universitaria Panamericana, Colombia), Ángela M. Sánchez Ramos (Fundación Universitaria Panamericana, Colombia)and Omar R. Moreno Cubides (Fundación Universitaria Panamericana, Colombia)
Copyright: 2020
Pages: 30
Source title:
Pattern Recognition Applications in Engineering
Source Author(s)/Editor(s): Diego Alexander Tibaduiza Burgos (Universidad Nacional de Colombia, Colombia), Maribel Anaya Vejar (Universidad Sergio Arboleda, Colombia)and Francesc Pozo (Universitat Politècnica de Catalunya, Spain)
DOI: 10.4018/978-1-7998-1839-7.ch007
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Abstract
This chapter presents a study to identify with classification techniques and digital recognition through the construction of a prototype phase that predicts criminal behavior detected in video cameras obtained from a free platform called MOTChallenge. The qualitative and descriptive approach, which starts from individual attitudes, expresses a person in his expression, anxiety, fear, anger, sadness, and neutrality through data collection and feeding of some algorithms for assisted learning. This prototype begins with a degree higher than 40% on a scale of 1-100 of a person suspected, subjected to a two- and three-iterations training parameterized into four categories—hood, helmet, hat, anxiety, and neutrality—where through orange and green boxes it is signaled at the time of the detection and classification of a possible suspect, with a stability of the 87.33% and reliability of the 96.25% in storing information for traceability and future use.
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