Online video-based abnormal detection using highly motion techniques and statistical measures

At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for he accuracy while attaining simultaneously low values for the processing time

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Diseño, implementación y evaluación de una estrategia de detección de objetos abandonados en aplicaciones de videovigilancia

Este trabajo aborda el estudio e implementación de algoritmos de aprendizaje profundo (Deep Learning) con la finalidad de detectar objetos abandonados en aplicaciones de videovigilancia. Se ha realizado un estudio teórico de los algoritmos de detección y seguimiento disponibles en el Estado del Arte. Para la detección de objetos en tiempo real se ha empleado YOLOv4 [1]. Como algoritmo de seguimiento se ha optado por Deep SORT [2]. Por último, se ha desarrollado un algoritmo que determine si un objeto ha sido abandonado o no. Todos ellos han sido implementados sobre el dataset de referencia MS COCO [3] y evaluados sobre los datasets más relevantes en la detección de objetos abandonados como son GBA2018 [4], PETS2007 [5], AVSSAB2007 [6] o ABODA [7].This Master’s Thesis proposes the study and implementation of Deep Learning algorithms in order to detect abandoned objects in video surveillance applications. A theoretical study of the detection and monitoring algorithms available in the State of the Art has been carried out. YOLOv4 [1] has been used to detect objects in real time. Deep SORT [2] has been chosen as tracking algorithm. Finally, an algorithm has been developed to determine when an object has been abandoned or not. All of them have been implemented on the MS COCO [3] benchmark dataset and evaluated on the most relevant datasets in the detection of abandoned objects such as GBA2018 [4], PETS2007 [5], AVSSAB2007 [6] or ABODA [7].Máster Universitario en Ingeniería Industrial (M 141