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MCPT: Multi-Camera People Tracking
- Author(s)
- 오주환
- Alternative Author(s)
- Kevin Joohwan Oh
- Advisor
- 김재훈
- Department
- 일반대학원 컴퓨터공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-02
- Language
- eng
- Keyword
- Multi Object Tracking; Object Detection
- Alternative Abstract
- People tracking and detection is a fundamental problem and goal that has applications in numerous fields. The rise of machine learning methods has put Computer Vision as the forefront method to tackle this problem. But it remains a challenging problem, due to the various variables in complex real-world scenes such as the number of involved people, the possibility of multiple occlusions, and the presence of cluttered and changing backgrounds. Modern people detectors show remarkable ability in locating and detecting pedestrians even in densely populated, cluttered scenes, but they still suffer from false positives and might come with other restrictions such as having a high computational cost to run. People tracking remains a task of high interest because of its many applications to multiple different industries and fields of research such as retail, security, or automation to name a few. But for people tracking to become widely and effectively used across these fields certain conditions must be met beyond just as a theoretical or research topic. The purpose of this thesis is to explore how to enable practical people tracking for real world use. That is a solution to people tracking that delivers acceptable results as cheaply as possible. Thus, in this paper we will be thoroughly exploring the tradeoff between performance and computational cost of different people tracking models and looking for ways to reduce that cost as much as possible with minimum sacrifices in performance. Such as through using Distributed Systems and simplified tracking paradigms.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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