A Review on Human Actions Recognition Using Vision Based Techniques <s> II. RELATED WORK <s> Abstract Motion-based recognition deals with the recognition of an object or its motion based on motion in a sequence of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consists of a complex and coordinated series of events that cannot be understood by looking at only a few frames. This paper provides a review of recent developments in the computer vision aspect of motionbased recognition. We will identify two main steps in motionbased recognition. The first step is the extraction of motion information and its organization into motion models. The second step consists of the matching of some unknown input with a constructed model. Several methods for the recognition of objects and motions will then be reported. They include methods such as cyclic motion detection and recognition, lipreading, hand gestures interpretation, motion verb recognition and temporal textures classification. Tracking and recognition of human motion, like walking, skipping and running will also be discussed. Finally, we will conclude the paper with some thoughts about future directions for motionbased recognition. <s> BIB001 </s> A Review on Human Actions Recognition Using Vision Based Techniques <s> II. RELATED WORK <s> Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man?machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: (1) motion analysis involving human body parts, (2) tracking a moving human from a single view or multiple camera perspectives, and (3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion from a single view or multiple perspectives focuses on higher-level processing, in which moving humans are tracked without identifying their body parts. After successfully matching the moving human image from one frame to another in an image sequence, understanding the human movements or activities comes naturally, which leads to our discussion of recognizing human activities. <s> BIB002 </s> A Review on Human Actions Recognition Using Vision Based Techniques <s> II. RELATED WORK <s> The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions. <s> BIB003
Cedras and Shah in 1995 BIB001 , Gavrila in 1999 BIB003 and Aggarwal and Cai in 1999 BIB002 produced the review on recognizing human actions. Kruger et al. in 2007 also discussed the recognition of human actions by arranging them on the basis of the complex features. Turaga et al. in 2008 [13] also furnished a survey related to vision based action recognition techniques. In all above surveys they categorize different techniques on the basis of complexity in actions. Since most of the previous reviews contain only introduction and summaries of various methods to recognize human actions but they do not compare various approaches. In our review, various methods require to recognize human actions. We used a standard taxonomy to compare every method. Furthermore our review not only covers recognizing methods but also the complex human actions in group form. And in the end we discuss the results received after applying various methods on the datasets available in public for the purpose to recognize various human actions.