Arquitectura de detección de actividades criminales basada en análisis de vídeo en tiempo real

[ES] Esta tesis doctoral propone el desarrollo de una arquitectura para sistema de detección de actividades criminales en vídeo aplicado a sistemas de mando y control para seguridad ciudadana. Este sistema está basado en la técnica de Deep Learning Faster R-CNN y tiene el novedoso enfoque de tratar las acciones criminales como los hurtos callejeros, en donde pueden ser identificados objetos como evidencia en una escena de vídeo. Esta tesis muestra el desarrollo de dicha aplicación, que demuestra ser efectiva, identificando la manera de reducir el costo computacional del análisis de vídeo cuadro a cuadro obteniendo rendimientos congruentes con las tasas de cuadros por segundo generados por cámaras de sistema de vídeo vigilancia ciudadana. También es objeto de estudio una posible implementación en el sistema de seguridad ciudadana de la Policía Nacional de Colombia.[EN] This doctoral thesis proposes the development of a system to detect criminal activities in video applied to command and control systems for citizen security. This system is based on the Deep Learning technique called Faster R-CNN and has the novel approach of treating criminal actions like street thefts as objects that can be identified in a video scene. This thesis shows the development of this application and the way to reduce the computational cost of the video analysis frame by frame, obtaining performances congruent with the frame rate generated by citizen video surveillance system cameras. There is also a possible implementation in the citizen security system of the National Police of Colombia is being studied.[CA] Esta tesi doctoral proposa el desenrotllament d'una arquitectura per a sistema de detecció d'activitats criminals en vídeo aplicat a sistemes de comandament i control per a seguretat ciutadana. Este sistema està basat en la tècnica de Deep Learning Faster R-CNN i té el nou enfocament de tractar les accions criminals com les afanades guies de carrers com a objectes que poden ser identificats en una escena de vídeo. Esta tesi mostra el desenrotllament de la dita aplicació, que demostra ser efectiva, identificant la manera de reduir el cost computacional de l'anàlisi de vídeo quadro a quadro obtenint rendiments congruents amb les taxes de cuados per segon generats per cambres de sistema de vídeo vigilància ciutadana. També s'estudia una possible implementació en el sistema de seguretat ciutadana de la Policia Nacional de Colòmbia.Suárez Páez, JE. (2020). Arquitectura de detección de actividades criminales basada en análisis de vídeo en tiempo real [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153162TESI

Enhancing audio surveillance with hierarchical recurrent neural networks

The need for effective and reliable surveillance techniques is getting nowadays more and more of primary importance, especially in the actual scenario in which safety and security have become a priority. While classical techniques rely on video-based surveillance systems, such as Close-Circuit television, many studies show that also the audio signal can be effectively used for these purposes. There are many characteristics that make the audio signal particularly suited for this task and, above all, the fact that the analysis of the audio signal can greatly improve thanks to the introduction of automatic classification. Recently, a large focus has been on the use of Deep Neural Networks for classifying audio data and, in this work, we aim to test their performance in the audio surveillance field. In this contribution we propose an algorithm for audio events detection in noisy environments based on the use of deep recurrent neural network. The achieved results show satisfactory and improved performances with respect to state-of-the-art techniques

Enhancing audio surveillance with hierarchical recurrent neural networks

The need for effective and reliable surveillance techniques is getting nowadays more and more of primary importance, especially in the actual scenario in which safety and security have become a priority. While classical techniques rely on video-based surveillance systems, such as Close-Circuit television, many studies show that also the audio signal can be effectively used for these purposes. There are many characteristics that make the audio signal particularly suited for this task and, above all, the fact that the analysis of the audio signal can greatly improve thanks to the introduction of automatic classification. Recently, a large focus has been on the use of Deep Neural Networks for classifying audio data and, in this work, we aim to test their performance in the audio surveillance field. In this contribution we propose an algorithm for audio events detection in noisy environments based on the use of deep recurrent neural network. The achieved results show satisfactory and improved performances with respect to state-of-the-art techniques