Browsing by Author "Ashraf, Abdelaziz"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Can pose classification be used to teach Kickboxing?
    (Institute of Electrical and Electronics Engineers Inc, 2021-12) Wessa, Eriny; Ashraf, Abdelaziz; Atia, Ayman
    Kickboxing is a combat sport, based on kicking, punching, Knee and elbow strikes and defence moves. Every kickboxing technique needs to be preformed a specific way, As there is correct postures and wrong postures to every technique. In this paper, we offer a system that can facilitate the beginners trainees to learn kickboxing. The system uses a camera to estimate poses and then, classify them into 'correct techniques' and their common mistakes or 'wrong pose' using ANN. Live feedback is offered by the system. Whenever the classifier recognize a wrong pose, a message is shown to indicate how to correct the posture. Our hypothesis is that, when trainees have the ability to see and recognize their wrong posters, they learn faster. We evaluate the progress of the trainees based on the time it takes to complete a simple kickboxing exercise. Two types of experiments were conducted. The first calculated the progress of trainees everyday, the other calculated the progress of trainees through three training sessions in the span on two hours. Our results show that time taken by users to preform the moves decrease with each time they use our system. This paper focuses on 3 kickboxing techniques, which are slipping, jab and front kick. © 2021 IEEE.
  • Thumbnail Image
    Item
    Visual Engagement: Quantifying Campus Experiences in Urban Open Spaces Using a Computer Vision Model
    (2024-05) Mohareb, Nabil; Ashraf, Abdelaziz
    Introduction Addressing the gap in quantitative analysis of spatial experiences within academic environments, this study introduces a groundbreaking framework designed to measure and quantify the visual experiences of individuals in academic campus settings. Focused on analyzing the visual composition of the built environment—including aspects such as visible sky, greenery, and spatial enclosure—our framework aims to provide a quantitative refl ection of the subjective spatial experiences of campus users. Methods The methodology involves using mobile phones with digital cameras and GPS sensors to capture firstperson visual data and track movements as they freely traverse campus open spaces. Computer vision techniques, including Instance segmentation and convolutional neural networks, will categorize architectural and natural elements within each frame image extracted from a recorded video, quantify proportional compositions and analyze relative amounts of greenery, open sky, walkways, buildings, and other built structures that participants visually experienced. The framework is translated into a Python model capable of producing quantitative outcomes. The analysis will be further enriched by integrating Geographic Information Systems (GIS) for spatial analysis to identify navigation and visual engagement patterns. This comprehensive methodology quantifi es the visual attributes of spaces and interprets their impact on the behavior and experiences of campus users. Results and conclusions The study outcomes reveal relationships between student’s navigation choices, visual experiences, and scene types. The results aim to guide urban designers in understanding university students’ open space needs based on their natural movement and viewing preferences and complement other qualitative approaches.