| CODE | ICS5117 | |||||||||
| TITLE | Intelligent Video Analytics | |||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | |||||||||
| MQF LEVEL | Not Applicable | |||||||||
| ECTS CREDITS | 6 | |||||||||
| DEPARTMENT | Intelligent Computer Systems | |||||||||
| DESCRIPTION | This study-unit will deal with methods for analyzing and interpreting the contents of video data, by reviewing existing state-of-the-art approaches and algorithms. The methods will be explored through their usage in real-world applications such as object tracking, summarization of video clips, action recognition, and others. The main topics include: - Mean Shift theory - Mean Shift Tracking - Object Detection & Tracking - Action Recognition - Shot Transition detection Study-unit Aims: The aims of this study-unit are to: - Introduce students to state-of-the-art techniques in the field of video analytics, specifically in object tracking, action recognition and shot detection. - Expose students to research streams in video analytics. - Provide hands-on experience with popular tools, such as Matlab or Python or C++ for image processing. The VLFeat and OpenCV libraries will be used. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Identify the right tools to address a given problem in video analytics. - Develop various solutions for the extraction of information in videos. - Implement a pipeline (a chain of processing elements) to analyse a video. - Compare different video analytic techniques. - Evaluate the performance of intelligent systems techniques in videos. 2. Skills: By the end of the study-unit the student will be able to: - Use any of the techniques covered to process different types of videos. - Compare the appropriateness of a number of object tracking, action recognition and shot detection to solve various problems. - Implement algorithms with state of the art libraries. - Study and understand literature. - Present research work in front of an audience. - Generate new ideas to address challenging video analytics problems. Main Text/s and any supplementary readings: Textbook: Computer Vision: Algorithms and Applications, by Rick Szeliski. A free electronic copy is available online "http://szeliski.org/Book/". |
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| ADDITIONAL NOTES | Pre-requisite Study-unit: CCE5221 Co-requisite Study-units: ICS5116, CCE5222 |
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| STUDY-UNIT TYPE | Lecture and Tutorial | |||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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