Ciencias Exactas y Ciencias de la Salud

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Detection of suspicious attitudes on video using neuroevolved shallow and deep neural networks models
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11) Flores Munguía, Carlos; Terashima Marín, Hugo; puemcuervo/tolmquevedo; Oliva, Diego; Ortiz Bayliss, Jose Carlos; School of Engineering and Sciences; Campus Monterrey
The analysis of surveillance cameras is a critical task usually limited by the people involved in the video supervision devoted to such a task, their knowledge, and their judgment. Security guards protect other people from different events that can compromise their security, like robbery, extortion, fraud, vehicle theft, and more, converting them to an essential part of this type of protection system. If they are not paying attention, crimes may be overlooked. Nonetheless, different approaches have arisen to automate this task. The methods are mainly based on machine learning and benefit from developing neural networks that extract underlying information from input videos. However, despite how competent those networks have proved to be, developers must face the challenging task of defining the architecture and hyperparameters that allow the network to work adequately and optimize the use of computational resources. Furthermore, selecting the architecture and hyperparameters may significantly impact the neural networks’ performance if it is not carried out adequately. No matter the type of neural network used, shallow, dense, convolutional, 3D convolutional, or recurrent; hyperparameter selection must be performed using empirical knowledge thanks to the expertise of the designer, or even with the help of automated approaches like Random Search or Bayesian Optimization. However, such methods suffer from problems like not covering the solution space well, especially if the space is made up of large dimensions. Alternatively, the requirement to evaluate the models many times to get more information about the evaluation of the objective function, employing a diverse set of hyperparameters. This work proposes a model that generates, through a genetic algorithm, neural networks for behavior classification within videos. The application of genetic algorithms allows the exploration in the hyperparameters solution space in different directions simultaneously. Two types of neural networks are evolved as part of the thesis work: shallow and deep networks, the latter based on dense layers and 3D convolutions. Each sort of network takes distinct input data types: the evolution of people’s pose and videos’ sequences, respectively. Shallow neural networks are generated by NeuroEvolution of Augmented Topologies (NEAT), while CoDeepNEAT generates deep networks. NEAT uses a direct encoding, meaning that each node and connection in the network is directly represented in the chromosome. In contrast, CoDeepNEAT uses indirect encoding, making use of cooperative coevolution of blueprints and modules. This work trains networks and tests them using the Kranok-NV dataset, which exhibited better results than their competitors on various standard metrics.
Detection of Violent Behavior in Open Environments Using Pose Estimation and Neural Networks
(Instituto Tecnológico y de Estudios Superiores de Monterrey) Chong Loo, Kevin Brian Kwan; TERASHIMA MARIN, HUGO; 65879; Terashima Marín, Hugo; tolmquevedo, emipsanchez; Conant Pablos, Santiago Enrique; Escuela de Ingeniería y Ciencia; Campus Monterrey
People’s safety and security have always been an issue to attend. With the coming of techno- logical advances, part of it has been used to improve safeguards, though other aspects, without precautions, have made people even more vulnerable. People can get their sensitive data stolen or become victims of transaction fraud. These may be crimes done without physical interac- tion, but felonies with physical violence still exist. Some solutions for pedestrian safety are guards, police cars patrolling, sensors and security cameras. Nonetheless, these methods only react when the crime is happening or, even more critical, when it has already occurred, and the damage has been done. Therefore, numerous methods have been implemented using Arti- ficial Intelligence in order to solve this problem. Many approaches to detect violent behavior and action recognition rely on 3D convolutional neural networks (3D CNNs), spatial tempo- ral models, long short term memory networks, pose estimation among other implementations. However, in the current state of the art, how these approaches are used do not work perfectly and are not adapted to an uncontrolled environment. Therefore, a significant contribution from this work was the development of a new solu- tion model that is able to detect violent behavior. This approach focuses on using pedestrian detection, tracking, pose estimation and neural networks to predict pedestrian behavior in video frames. This method uses a time window frame to extract joint angles, given by the pose estimation algorithm, as features for classifying behavior. At the moment of developing this thesis project, there were not many databases with violent behavior videos. The ones that existed were low quality; cluttered were pedestrians cannot be seen clearly, and with unfixed camera angles. Consequently, another important contribution of this work was creating a new database, Kranok-NV, with a total of 3,683 normal and violent videos. This database was used to train and test the solution model. For the evaluation, a protocol was designed using 10-fold cross- validation. With the implemented solution model, accuracy of more than 98% was achieved on the Kranok-NV database. This approach surpassed the performance of state of the art methods for violence detection and action recognition in the developed database. Though this new solution model is able to detect violent and normal behavior, it can be easily extended to classify more types of behaviors. Further work requires to test this approach in emerging databases of videos and optimize specific areas of the solution model. Additionally, the contributions of this work can aid in the development of new approaches.

Ciencias Exactas y Ciencias de la Salud

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