Multi-modal tracking of faces for video communications

Crowley, James L.; Bérard, François

doi:10.1109/cvpr.1997.609393

Cited by 157 publications

(90 citation statements)

References 7 publications

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“…The first methods for rigid 2D tracking generally revolved around the use of various features or transformations and mainly explored various color-spaces for robust tracking (Crowley and Berard 1997;Bradski 1998b;Qian et al 1998;Toyama 1998;Jurie 1999;Schwerdt and Crowley 2000;Stern and Efros 2002;Vadakkepat et al 2008). The general methods of choice for tracking were Mean Shift and variations such as the Continuously Adaptive Mean Shift (Camshift) algorithm (Bradski 1998a;Allen et al 2004).…”

Section: Prior Artmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Prior Artmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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“…A popular approach is to work in an intensity-normalized colour space. However, this alone is often not sufficient and a method that is able to initialize and adapt the colour model is required [17,18,5,15]. In this paper the skin colour model is obtained from a frontal face detector, which is run at every kth frame (k = 30) and which does not rely on colour information [19].…”

Section: Colour Modelmentioning

confidence: 99%

“…Motion is another valuable cue that has been extensively used in HCI applications [18] as it is robust under a wide range of conditions. The motion feature z M is computed from the difference image as the L 1 -norm of the pixel-wise RGB difference vectors at times t and t − 1:…”

Section: Motion Modelmentioning

confidence: 99%

Template-Based Hand Pose Recognition Using Multiple Cues

Stenger

2006

Computer Vision – ACCV 2006

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Abstract. This paper presents a practical method for hypothesizing hand locations and subsequently recognizing a discrete number of poses in image sequences. In a typical setting the user is gesturing in front of a single camera and interactively performing gesture input with one hand. The approach is to identify likely hand locations in the image based on discriminative features of colour and motion. A set of exemplar templates is stored in memory and a nearest neighbour classifier is then used for hypothesis verification and pose estimation. The performance of the method is demonstrated on a number of example sequences, including recognition of static hand gestures and a navigation by pointing application.

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“…A general discussion of the use of the Kalman filter for sensor fusion is given in [30]. The use of the Kalman filter for tracking faces is described in [31].…”

Section: Examples: Modules For Observing Grouping and Trackingmentioning

confidence: 99%

Context Driven Observation of Human Activity

Crowley

2003

Lecture Notes in Computer Science

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Abstract. 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".

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Multi-modal tracking of faces for video communications

Abstract: This paper describes a system which uses multiple visual processes to detect

Cited by 157 publications

References 7 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Template-Based Hand Pose Recognition Using Multiple Cues

Context Driven Observation of Human Activity

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