Ciencias Exactas y Ciencias de la Salud

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End-to-End Violence Detection Using Pedestrian Detection, Pose Estimation, and Temporal GRUs for Surveillance Applications
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-05-27) Salazar Vasquez, Fredy Antonio; Conant Pablos, Santiago Enrique; emipsanchez; Ortiz bayliss, José Carlos; School of Engineering and Sciences; Campus Monterrey
In recent years, surveillance systems have played an increasingly prominent role in both public and private settings. These systems monitor activities in real time and provide information to security personnel and authorities. Their constant observation helps prevent incidents and maintain order. Traditional surveillance systems record events but do not fully exploit the valuable information they capture. New technologies allow valuable data to be extracted, turning surveillance into an active tool for security. With the development of tools like object detection, pose estimation, and neural networks, surveillance systems can now interpret the scenes they capture. Rather than simply recording footage, these systems are becoming active participants in security by extracting meaningful information from visual data. Despite these advances, it remains a challenge to identify violent acts using visual information. The main challenge is to analyze the data in a way that identifies risks. Although cameras capture lot of information, traditional systems do not always use them preventively. These systems must predict risky situations by detecting aggressive behavior or suspicious activities early. This work primarily focuses on addressing the development of techniques to improve the detection of violence in surveillance videos by optimizing specific processes such as pedestrian detection, human posture estimation, object tracking, and violent behavior classification. Pedestrian detection is optimized using advanced models like YOLO, enhancing accuracy in high-density environments. Posture estimation is improved through advanced pose detection algorithms that reduce manual intervention. Object tracking is enhanced by implementing Deep SORT to maintain reliable identity tracking across video frames. Violent behavior classification is fine-tuned using a deep neural network architecture based on Gated Recurrent Units (GRU), which captures temporal movement patterns. Video footage from the KranokNV database is processed to identify joint angles of pedestrians, and the VID dataset is used to evaluate system performance. This integrated approach aims to achieve faster, more accurate, and more reliable detection of violent situations, contributing to public safety. Additionally, the evaluation considers spatial and temporal features, such as velocity, acceleration, motion energy, abrupt changes, symmetry, and expansion radius. The processed data was smoothed with the Kalman filter, achieving an accuracy of 99.44%. The results indicate continuous detection capability and improvement in generalization throughout the training process.
Aspect based sentiment analysis in students’ evaluation of teaching
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Acosta Ugalde, Diego; Conant Pablos, Santiago Enrique; mtyahinojosa, emipsanchez; Guitérrez Rodríguez, Andrés Eduardo; Juárez Jiménez, Julio Antonio; Morales Méndez, Rubén; School of Engineering and Sciences; Campus Monterrey; Camacho Zuñiga, Claudia
Student evaluations of teachings (SETs) are essential for assessing educational quality. Natural Language Processing (NLP) techniques can produce informative insights from these evaluations. The large quantity of text data received from SETs has surpassed the capacity for manual processing. Employing NLP to analyze student feedback offers an efficient method for understanding educational experiences, enabling educational institutions to identify patterns and trends that might have been difficult, if not impossible, to notice with a manual analysis. Data mining using NLP techniques can delve into the thoughts and perspectives of students on their educational experiences, identifying sentiments and aspects that may have a level of abstraction that the human analysis cannot perceive. I use different NLP techniques to enhance the analysis of student feedback in the form of comments and provide better insights and understanding into factors that influence students’ sentiments. This study aims to provide an overview of the various approaches used in NLP and sentiment analysis, focusing on analyzing the models and text representations used to classify numerical scores obtained from the text feedback of a corpus of SETs in Spanish. I provide a series of experiments using different text classification algorithms for sentiment classification over numerical scores of educational aspects. Additionally, I explore two Aspect Based Sentiment Analysis (ABSA) models, a pipeline and a multi-task approach, to extract broad and comprehensive insights from educational feedback for each professor. The results of this research demonstrate the effectiveness of using NLP techniques for analyzing student feedback. The sentiment classification experiments showed favorable outcomes, indicating that it is possible to utilize student comments to classify certain educational scores accurately. Furthermore, the qualitative results obtained from the ABSA models, presented in a user-friendly dashboard, highlight the efficiency and utility of employing these algorithms for the analysis of student feedback. The dashboard provides valuable insights into the sentiments expressed by students regarding various aspects of their educational experience, allowing for a more comprehensive understanding of the factors influencing their opinions. These findings highlight the potential of NLP in the educational domain, offering a powerful tool for institutions to gain a deeper understanding of student perspectives and make data-driven decisions to enhance the quality of education.

Ciencias Exactas y Ciencias de la Salud

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