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Computer vision and image processing-based systems and their applications are already an integral part of modern living and are expected to increase in prevalence and complexity. Vision system provides the ability to handle and examine the large data generated by cameras and make a decision based on the situational requirement. As computational intelligent methods are especially adept at rapidly resolving inexact situations or where there is incomplete knowledge, they are being heavily researched and employed in this space. This merger creates intelligent vision systems, which can be extremely versatile, and this book focusses on the latest developments and current key research areas in the field.
Artificial Intelligence (AI) tasks that would normally require human eyesight are best served by computer vision. As a result, people counting, usually referred as crowd counting, is an important computer vision application. People counting is a technique for counting people in public or estimating the amount of people in a certain area. Manually restricting the number of people in public places is tedious. Hence, the Chapter 3 proposes a crowd management technique using a machine learning approach. The proposed system counts the people in a video frame captured from CCTV installed in public places. A deep neural based network is used for crowd computing in real time. The work uses the YOLOV3 model which is trained using the COCO dataset to detect the people in the frame. Even in difficult situations, such as crowded heads and incomplete visibility of heads, the model performs well. In a densely populated area this method provides high accuracy in determining the human count within a predetermined time frame and the accuracy achieved by the model is 95%.
Key Features:
Interdisciplinary approach to intelligent computing applications for machine vision
Encompasses high performance computing for vision systems and control
Includes present applications and challenges for future development
Reviews range of Computational Intelligence (CI) and Machine Learning (ML) methodologies
International author pool