http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

https://www.ee.columbia.edu/%7Eajaimes/Pubs/ajaimes_iccv05.pdf

Multimodal human-computer interaction: A survey

http://www2.imm.dtu.dk/projects/manifold/Papers/Vision-based%20human%20motion.pdf

Vision-based human motion analysis: An overview

Architecture for implementing a perceptual user interface. The architecture comprises alternative modalities for controlling computer application programs and manipulating on-screen objects through hand gestures or a combination of hand gestures and verbal commands. The perceptual user interface system includes a tracking component that detects object characteristics of at least one of a plurality of objects within a scene, and tracks the respective object. Detection of object characteristics is based at least in part upon image comparison of a plurality of images relative to a course mapping of the images. A seeding component iteratively seeds the tracking component with object hypotheses based upon the presence of the object characteristics and the image comparison. A filtering component selectively removes the tracked object from the object hypotheses and/or at least one object hypothesis from the set of object hypotheses based upon predetermined removal criteria.

Architecture for controlling a computer using hand gestures

Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing.

Vision-based hand-gesture applications

Emergency response requires strategic assessment of risks, decisions, and communications that are time critical while requiring teams of individuals to have fast access to large volumes of complex information and technologies that enable tightly coordinated work. The access to this information by crisis management teams in emergency operations centers can be facilitated through various human-computer interfaces. Unfortunately, these interfaces are hard to use, require extensive training, and often impede rather than support teamwork. Dialogue-enabled devices, based on natural, multimodal interfaces, have the potential of making a variety of information technology tools accessible during crisis management. This paper establishes the importance of multimodal interfaces in various aspects of crisis management and explores many issues in realizing successful speech-gesture driven, dialogue-enabled interfaces for crisis management. This paper is organized in five parts. The first part discusses the needs of crisis management that can be potentially met by the development of appropriate interfaces. The second part discusses the issues related to the design and development of multimodal interfaces in the context of crisis management. The third part discusses the state of the art in both the theories and practices involving these human-computer interfaces. In particular, it describes the evolution and implementation details of two representative systems, Crisis Management (XISM) and Dialog Assisted Visual Environment for Geoinformation (DAVE/spl I.bar/G). The fourth part speculates on the short-term and long-term research directions that will help addressing the outstanding challenges in interfaces that support dialogue and collaboration. Finally, the fifth part concludes the paper.

Speech-gesture driven multimodal interfaces for crisis management

This paper presents a framework for designing a natural multimodal human computer interaction (HCI) system. The core of the proposed framework is a principled method for combining information derived from audio and visual cues. To achieve natural interaction, both audio and visual modalities are fused along with feedback through a large screen display. Careful design along with due considerations of possible aspects of a systems interaction cycle and integration has resulted in a successful system. The performance of the proposed framework has been validated through the development of several prototype systems as well as commercial applications for the retail and entertainment industry. To assess the impact of these multimodal systems (MMS), informal studies have been conducted. It was found that the system performed according to its specifications in 95% of the cases and that users showed ad-hoc proficiency, indicating natural acceptance of such systems.

A real-time framework for natural multimodal interaction with large screen displays

This paper presents a multimodal crisis management system (XISM). It employs processing of natural gesture and speech commands elicited by a user to efficiently manage complex dynamic emergency scenarios on a large display. The developed prototype system demonstrates the means of incorporating unconstrained free-hand gestures and speech in a real-time interactive interface. This paper provides insights into the design aspects of the XISM system. In particular, it addresses the issues of extraction and fusion of gesture and speech modalities to allow more natural interactive behavior. Performance characteristics of the current prototype and considerations for future work are discussed. A series of studies indicated positive response with respect to ease of interacting with the current system.

/pdf/multimodal-human-computer-interaction-for-crisis-management-3sv3c3kwid.pdf

Multimodal human-computer interaction for crisis management systems

Tracking, classification and visual analysis of articulated motion is challenging because of the difficulties involved in separating noise and variabilities caused by appearance, size and viewpoint fluctuations from task-relevant variations. By incorporating powerful domain knowledge, model-based approaches are able to overcome these problem to a great extent and are actively explored by many researchers. However, model acquisition, initialization and adaptation are still relatively under-investigated problems, especially for the case of single-camera systems.In this paper, we address the problem of automatic acquisition and initialization of articulated models from monocular video without any prior knowledge of shape and kinematic structure. The framework is applied in a human-computer interaction context where articulated shape models have to be acquired from unknown users for subsequent limb tracking. Bayesian motion segmentation is used to extract and initialize articulated models from visual data. Image sequences are decomposed into rigid components that can undergo parametric motion. The relative motion of these components is used to obtain joint information. The resulting components are assembled into an articulated kinematic model which is then used for visual tracking, eliminating the need for manual initialization or adaptation. The efficacy of the method is demonstrated on synthetic as well as natural image sequences. The accuracy of the joint estimation stage is verified on ground truth data.

/pdf/automatic-acquisition-and-initialization-of-articulated-4084xccv4h.pdf

Automatic acquisition and initialization of articulated models

This paper presents a systematic approach to acquiring model appearance information online for monocular model-based tracking. The acquired information is used to drive a set of complementary imaging cues to obtain a highly discriminatory observation model. Appearance is modeled as a Markov random field of color distributions over the model surface. The online acquisition process estimates appearance-based on uncertain image measurements and is designed to greatly reduce the chance of mapping non-object image data onto the model. Confidences about the different appearance driven imaging cues are estimated in order to adaptively balance the contributions of the different cues. The discriminatory power of the resulting model is good enough to allow long-duration single-hypothesis model-based tracking with no prior appearance information. Careful evaluation based on real and semi-synthetic video sequences shows that the presented algorithm is able to robustly track a wide variety of targets under challenging conditions.

N. Krahnstoever

Papers

Speech-gesture driven multimodal interfaces for crisis management

A real-time framework for natural multimodal interaction with large screen displays

Multimodal human-computer interaction for crisis management systems

Automatic acquisition and initialization of articulated models

Appearance management and cue fusion for 3D model-based tracking