An Architecture for Context Aware Observation of Human Activity (original) (raw)
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Context Driven Observation of Human Activity
Lecture Notes in Computer Science, 2003
Human activity is extremely complex. Current technology allows us to handcraft real-time perception systems for a specific perceptual task. However, such an approach is inadequate for building systems that accommodate the variety that is typical of human environments. In this paper we define a framework for context aware observation of human activity. A context in this framework is defined as a network of situations. A situation network is interpreted as a specification for a federation of processes to observe humans and their actions. We present a process-based software architecture for building systems for observing activity. We discuss methods for building systems using this framework. The framework and methods are illustrated with examples from observation of human activity in an "Augmented Meeting Environment".
Dynamic approach for real-time skin detection
Journal of Real-Time Image Processing, 2012
Human face and hand detection, recognition and tracking are important research areas for many computer interaction applications. Face and hand are considered as human skin blobs, which fall in a compact region of colour spaces. Limitations arise from the fact that human skin has common properties and can be defined in various colour spaces after applying colour normalization. The model therefore, has to accept a wide range of colours, making it more susceptible to noise. We have addressed this problem and propose that ...
Encyclopedia of Biometrics, 2009
Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approaches have been developed for skin detection. A skin detector typically transforms a given pixel into an appropriate color space and then use a skin classifier to label the pixel whether it is a skin or a non-skin pixel. A skin classifier defines a decision boundary of the skin color class in the color space based on a training database of skin-colored pixels. †
Tracking Skin-Colored Objects in Real-Time
Cutting Edge Robotics, 2005
We present a methodology for tracking multiple skin-colored objects in a monocular image sequence. The proposed approach encompasses a collection of techniques that allow the modeling, detection and temporal association of skincolored objects across image sequences. A non-parametric model of skin color is employed. Skin-colored objects are detected with a Bayesian classifier that is bootstrapped with a small set of training data and refined through an off-line iterative training procedure. By using on-line adaptation of skin-color probabilities the classifier is able to cope with considerable illumination changes. Tracking over time is achieved by a novel technique that can handle multiple objects simultaneously. Tracked objects may move in complex trajectories, occlude each other in the field of view of a possibly moving camera and vary in number over time. A prototype implementation of the developed system operates on 320x240 live video in real time (28Hz), running on a conventional Pentium IV processor. Representative experimental results from the application of this prototype to image sequences are also presented.
Real-Time Tracking of Multiple Skin-Colored
2004
This paper presents a method for tracking multiple skincolored objects in images acquired by a possibly moving camera. The proposed method encompasses a collection of techniques that enable the modeling and detection of skin-colored objects as well as their temporal association in image sequences. Skin-colored objects are detected with a Bayesian classifier which is bootstrapped with a small set of training data. Then, an off-line iterative training procedure is employed to refine the classifier using additional training images. On-line adaptation of skin-color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique which can handle multiple skin-colored objects. Such objects may move in complex trajectories and occlude each other in the field of view of a possibly moving camera. Moreover, the number of tracked objects may vary in time. A prototype implementation of the developed system operates on 320x240 live video in real time (28Hz) on a conventional Pentium 4 processor. Representative experimental results from the application of this prototype to image sequences are also provided.
Lecture Notes in Computer Science, 2003
We describe a software development approach for vision that enhances robustness by making novel use of context. Conventional approaches to most image understanding problems suffer from fragility when applied to natural environments. Complexity in Intelligent Systems can be managed by breaking the world into manageable contexts. GRAVA supports robust performance by treating changes in the program's environment as context changes. Automatically tracking changes in the environment and making corresponding changes in the running program allows the program to operate robustly. We describe the software architecture and explain how it achieves robustness. GRAVA is a reflective architecture that supports self-adaptation and has been successfully applied to a number of visual interpretation domains. This paper describes the protocols and the interpreter for GRAVA.
Real-Time Computer Vision Processing on a Wearable System
2011
The proposed project involves the development of a wearable system that is capable of processing visual imagery from a camera in real-time and providing the user with actionable information. The system will be evaluated based on the image processing frame rate and its ability to detect obstacles.
An Analysis of Skin Detection and Segmentation
Skin segmentation is a fundamental pre-processing step for detecting, recognizing and tracking hands and faces. Different skin detections methods have been proposed and utilized with different colour spaces over the years. This paper presents a comparative evaluation of three skin detection methods, explicitly define skin region method, histogram based method and Bayesian Classifier method. In these methods, the image must first be converted to a suitable colour space that is less sensitive to illumination changes. The choice of the colour space would affect the accuracy of the results, thus analysis would be done on the popular colour spaces used. Each skin detection method would be analysed and compared.
Skin detection in video under changing illumination conditions
Pattern Recognition, …, 2000
Techniques for color-based tracking of faces or hands often assume a static skin color model. However, skin color perceived by a camera can change when lighting changes. In common real environment multiple light sources impinge on the skin. Therefore, for robust skin pixel detection, a dynamic skin color model that can cope with the changes must be employed. We show that skin detection in video can be enhanced by exploiting the knowledge of the range of possible skin colors for the camera used. In normalized color coordinates this range has a distinct shape we call the skin locus. We developed an adaptive histogram backprojection technique where the skin color model is updated by pixels in the search region which fall in the skin locus. We demonstrate increased detection capability with webcam videos of faces taken successively under daylight, incandescent lamp, fluorescent light and a combination of these light sources.
Skin detection in video under uncontrolled illumination
Multim. Tools Appl., 2021
Many vision-based human-computer interaction (HCI) applications require skin detection. However, their performance relies on accuracy in detecting skin regions in video, which is difficult under uncontrolled illumination. The chromatic appearance of skin changes because of shading, often caused by body movement. To address this, we propose a dynamic adaptation method to detect skin regions affected by local color deformations. Static and dynamic skin regions are detected by a corresponding module. The static module includes a facial skin distribution model (FSDM) and a fusion-based background distribution model (FBDM). The FBDM is obtained from a local background distribution model (LBDM) and a global background distribution model (GBDM). The LBDM is obtained by comparing a frame pixel distribution model with the FSDM and GBDM. Next, the FBDM is derived from the LBDM and the GBDM. The dynamic module includes a moving skin distribution model (MSDM), derived from a set of moving skin ...