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Movement, Activity, and Action: The Role of Knowledge in the Perception of Motion
- Royal Society Workshop on Knowledge-based Vision in Man and Machine
, 1997
"... We present several approaches to the machine perception of motion and discuss the role and levels of knowledge in each. In particular we describe different techniques of motion understanding as focusing on one of movement, activity, or action. Movements are the most atomic primitives, requiring no c ..."
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Cited by 69 (3 self)
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We present several approaches to the machine perception of motion and discuss the role and levels of knowledge in each. In particular we describe different techniques of motion understanding as focusing on one of movement, activity, or action. Movements are the most atomic primitives, requiring no contextual or sequence knowledge to be recognized; movement is often addressed using either view- invariant or view specific geometric techniques. Activity refers to sequences of movements or states, where the only real knowledge required is the statistics of the sequence; much of the recent work in gesture understanding falls within this category of motion perception. Finally, actions are larger scale events which typically include interaction with the environment and causal relationships; action understanding straddles the gray division between perception and cognition, computer vision and artificial intelligence. We illustrate these levels with examples drawn mostly from our work in unders...
Dual-state parametric eye tracking
- Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on
, 2000
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The Acquisition and Use of Interaction Behaviour Models
, 1997
"... Providing the machine with the ability to learn and use models of natural interaction is a challenging and largely unaddressed problem. A framework is developed enabling both the acquisition of interaction behaviours from the observation of humans, and the use of the acquired behaviour models to sim ..."
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Cited by 45 (7 self)
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Providing the machine with the ability to learn and use models of natural interaction is a challenging and largely unaddressed problem. A framework is developed enabling both the acquisition of interaction behaviours from the observation of humans, and the use of the acquired behaviour models to simulate a plausible partner during interaction. Statistically based interaction behaviour models are acquired automatically from the observation of interacting humans. Interaction with a virtual human is achieved using the model together with a stochastic tracking algorithm. Experimental results demonstrate the generation and use of the model for a simple human interaction. 1. Introduction In recent years many researchers have become interested in the development of techniques to allow a more natural form of interface between the user and the machine, utilising interactive spaces equipped with cameras and microphones where such techniques can be developed and tested (see, for example, [10]). ...
Appearance-Based Hand Sign Recognition from Intensity Image Sequences
, 2000
"... In this paper, we present a new approach to recognizing hand signs. In this approach, motion recognition (the hand movement) is tightly coupled with spatial recognition (hand shape). The system uses multiclass, multidimensional discriminant analysis to automatically select the most discriminating ..."
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Cited by 35 (1 self)
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In this paper, we present a new approach to recognizing hand signs. In this approach, motion recognition (the hand movement) is tightly coupled with spatial recognition (hand shape). The system uses multiclass, multidimensional discriminant analysis to automatically select the most discriminating linear features for gesture classification. A recursive partition tree approximator is proposed to do classification. This approach combined with our previous work on hand segmentation forms a new framework which addresses the three key aspects of hand sign interpretation: the hand shape, the location, and the movement. The framework has been tested to recognize 28 different hand signs. The experimental results show that the system achieved a 93.2% recognition rate for test sequences that have not been used in the training phase. It is shown that our approach provides better performance than the nearest neighbor classification in the eigen-subspace. 1 1 Introduction The ability to i...
Summarization of Video-taped Presentations: Automatic Analysis of Motion and Gesture
- IEEE Trans. on Circuits and Systems for Video Technology
, 1998
"... This paper presents an automatic system for analyzing and annotating video sequences of technical talks. Our method uses a robust motion estimation technique to detect key frames and segment the video sequence into subsequences containing a single overhead slide. The subsequences are stabilized to r ..."
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Cited by 29 (0 self)
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This paper presents an automatic system for analyzing and annotating video sequences of technical talks. Our method uses a robust motion estimation technique to detect key frames and segment the video sequence into subsequences containing a single overhead slide. The subsequences are stabilized to remove motion that occurs when the speaker adjusts their slides. Any changes remaining between frames in the stabilized sequences may be due to speaker gestures such as pointing or writing and we use active contours to automatically track these potential gestures. Given the constrained domain we define a simple set of actions that can be recognized based on the active contour shape and motion. The recognized actions provide an annotation of the sequence that can be used to access a condensed version of the talk from a web page.
Computers Seeing People
- AI Magazine
, 1999
"... this paper, we present methods that give machines the ability to see people, interpret their actions and interact with them. We present the motivating factors behind this work, examples of how such computational methods are developed and their applications. The basic reason for providing machines th ..."
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Cited by 24 (1 self)
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this paper, we present methods that give machines the ability to see people, interpret their actions and interact with them. We present the motivating factors behind this work, examples of how such computational methods are developed and their applications. The basic reason for providing machines the ability to see people really depends on the task we are associating with a machine. An industrial vision system aimed at extracting defects on an assembly line need not know anything about people. Similarly, a computer used for email and text writing need not see and perceive the users gestures and expressions. However, if our interest is to build intelligent machines that can work with us, support our needs and be our helpers, than it maybe required for these machines to know more about who they are supporting and helping. If our computers are to do more then support our text-based needs like writing papers, spreadsheets, and communicating via email; perhaps take on a role of being a personal assistant, then the ability to see a person is essential. Such an ability to perceive people is something that we take for granted in our everyday interactions with each other. At present our model of a machine or more specifically of a computer is something that is placed in the corner of the room. It is deaf, dumb, and blind, having no sense of the environment that it is in or of the person that is near it. We communicate with this computer using a coded sequence of tappings on a keyboard. Imagine a computer that knows that you are near it, that you are looking at it, knows who you are and what you are trying to do. Imagine a machine that can interpret a video signal based on who is in the scene and what they are doing. Such abilities in a computer are hard to imagine, unless it has...
Active vision techniques for visually mediated interaction. Image and Vision Computing
, 2002
"... In this paper we introduce adaptive vision techniques used, for example, in video-conferencing applications. Radial Basis Function (RBF) networks have been trained for gesture-based communication with colour/motion cues to direct face detection and capture ‘attentional frames’. These focus the proce ..."
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Cited by 23 (2 self)
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In this paper we introduce adaptive vision techniques used, for example, in video-conferencing applications. Radial Basis Function (RBF) networks have been trained for gesture-based communication with colour/motion cues to direct face detection and capture ‘attentional frames’. These focus the processing for Visually Mediated Interaction via an appearance-based approach with Gabor filter coefficients used as input to time-delay RBF networks. We use these methods for behaviour (user-camera) coordination in an integrated system. 1.
Generative Models for Learning and Understanding Dynamic Scene Activity
- in ECCV Workshop on Generative Model Based Vision
, 2002
"... We are entering an era of more intelligent cognitive vision systems. Such systems can analyse activity in dynamic scenes to compute conceptual descriptions from motion trajectories of moving people and the objects they interact with. Here we review progress in the development of flexible, generative ..."
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Cited by 23 (2 self)
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We are entering an era of more intelligent cognitive vision systems. Such systems can analyse activity in dynamic scenes to compute conceptual descriptions from motion trajectories of moving people and the objects they interact with. Here we review progress in the development of flexible, generative models that can explain visual input as a combination of hidden variables and can adapt to new types of input. Such models are particularly appropriate for the tasks posed by cognitive vision as they incorporate learning as well as having sufficient structure to represent a general class of problems. In addition, generative models explain all aspects of the input rather than attempting to ignore irrelevant sources of variation as in exemplar-based learning. Applications of these models in visual interaction for education, smart rooms and cars, as well as surveillance systems is also briefly reviewed.
Computers Seeing Action
- IN BRITISH MACHINE VISION CONFERENCE
, 1996
"... As research in computer vision has shifted from only processing single, static images to the manipulation of video sequences, the concept of action recognition has become important. Fundamental to understanding action is reasoning about time, in either an implicit or explicit framework. In this ..."
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Cited by 22 (1 self)
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As research in computer vision has shifted from only processing single, static images to the manipulation of video sequences, the concept of action recognition has become important. Fundamental to understanding action is reasoning about time, in either an implicit or explicit framework. In this paper I describe several specific examples of incorporating time into representations of action and how those representations are used to recognize actions. The approaches differ on whether variation over time is considered a continuous mapping, a state-based trajectory, or a qualitative, semantically labeled sequence. For two of the domains --- whole body actions and hand gestures --- I described the approaches in detail while two others --- constrained semantic domains (e.g. watching someone cooking) and labeling dynamic events (e.g. American football) --- are briefly mentioned.
Video analysis of human dynamics– a survey
- Real-Time Imaging
, 2003
"... Video analysis of human dynamics is an important area of research devoted to detecting people and understanding their dynamic physical behavior in a complex environment that can be used for biometric applications. This paper provides a detailed survey of the various studies in areas related to the t ..."
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Cited by 21 (0 self)
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Video analysis of human dynamics is an important area of research devoted to detecting people and understanding their dynamic physical behavior in a complex environment that can be used for biometric applications. This paper provides a detailed survey of the various studies in areas related to the tracking of people and body parts such as face, hands, fingers, legs, etc., and modeling behavior using motion analysis. 1.
