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Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms

Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms

Nancy E. Ochoa Guevara, Andres Esteban Puerto Lara, Nelson F. Rosas Jimenez, Wilmar Calderón Torres, Laura M. Grisales García, Ángela M. Sánchez Ramos, Omar R. Moreno Cubides
Copyright: © 2020 |Pages: 30
ISBN13: 9781799818397|ISBN10: 179981839X|ISBN13 Softcover: 9781799818403|EISBN13: 9781799818410
DOI: 10.4018/978-1-7998-1839-7.ch007
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MLA

Ochoa Guevara, Nancy E., et al. "Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms." Pattern Recognition Applications in Engineering, edited by Diego Alexander Tibaduiza Burgos, et al., IGI Global, 2020, pp. 150-179. https://doi.org/10.4018/978-1-7998-1839-7.ch007

APA

Ochoa Guevara, N. E., Puerto Lara, A. E., Rosas Jimenez, N. F., Calderón Torres, W., Grisales García, L. M., Sánchez Ramos, Á. M., & Moreno Cubides, O. R. (2020). Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms. In D. Burgos, M. Vejar, & F. Pozo (Eds.), Pattern Recognition Applications in Engineering (pp. 150-179). IGI Global. https://doi.org/10.4018/978-1-7998-1839-7.ch007

Chicago

Ochoa Guevara, Nancy E., et al. "Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms." In Pattern Recognition Applications in Engineering, edited by Diego Alexander Tibaduiza Burgos, Maribel Anaya Vejar, and Francesc Pozo, 150-179. Hershey, PA: IGI Global, 2020. https://doi.org/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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