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Vision-based human action recognition is the process of labeling image sequences with action labels. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field.
Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research. Check if you have access through your login credentials or your institution. This article has multiple issues.
Statements consisting only of original research should be removed. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. In 1966, it was believed that this could be achieved through a summer project, by attaching a camera to a computer and having it “describe what it saw”.
The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. By the 1990s, some of the previous research topics became more active than the others. Progress was made on the dense stereo correspondence problem and further multi-view stereo techniques. Recent work has seen the resurgence of feature-based methods, used in conjunction with machine learning techniques and complex optimization frameworks. A detailed understanding of these environments is required to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Consequently, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.