1,150 research outputs found
Machine Analysis of Facial Expressions
No abstract
AudioāVisual Speaker Tracking
Target motion tracking found its application in interdisciplinary fields, including but not limited to surveillance and security, forensic science, intelligent transportation system, driving assistance, monitoring prohibited area, medical science, robotics, action and expression recognition, individual speaker discrimination in multiāspeaker environments and video conferencing in the fields of computer vision and signal processing. Among these applications, speaker tracking in enclosed spaces has been gaining relevance due to the widespread advances of devices and technologies and the necessity for seamless solutions in realātime tracking and localization of speakers. However, speaker tracking is a challenging task in realālife scenarios as several distinctive issues influence the tracking process, such as occlusions and an unknown number of speakers. One approach to overcome these issues is to use multiāmodal information, as it conveys complementary information about the state of the speakers compared to singleāmodal tracking. To use multiāmodal information, several approaches have been proposed which can be classified into two categories, namely deterministic and stochastic. This chapter aims at providing multimedia researchers with a stateāofātheāart overview of tracking methods, which are used for combining multiple modalities to accomplish various multimedia analysis tasks, classifying them into different categories and listing new and future trends in this field
Particle Filters for Colour-Based Face Tracking Under Varying Illumination
Automatic human face tracking is the basis of robotic and active vision systems used for facial feature analysis, automatic surveillance, video conferencing, intelligent transportation, human-computer interaction and many other applications. Superior human face tracking will allow future safety surveillance systems which monitor drowsy drivers, or patients and elderly people at the risk of seizure or sudden falls and will perform with lower risk of failure in unexpected situations. This area has actively been researched in the current literature in an attempt to make automatic face trackers more stable in challenging real-world environments. To detect faces in video sequences, features like colour, texture, intensity, shape or motion is used. Among these feature colour has been the most popular, because of its insensitivity to orientation and size changes and fast process-ability. The challenge of colour-based face trackers, however, has been dealing with the instability of trackers in case of colour changes due to the drastic variation in environmental illumination. Probabilistic tracking and the employment of particle filters as powerful Bayesian stochastic estimators, on the other hand, is increasing in the visual tracking field thanks to their ability to handle multi-modal distributions in cluttered scenes. Traditional particle filters utilize transition prior as importance sampling function, but this can result in poor posterior sampling. The objective of this research is to investigate and propose stable face tracker capable of dealing with challenges like rapid and random motion of head, scale changes when people are moving closer or further from the camera, motion of multiple people with close skin tones in the vicinity of the model person, presence of clutter and occlusion of face. The main focus has been on investigating an efficient method to address the sensitivity of the colour-based trackers in case of gradual or drastic illumination variations. The particle filter is used to overcome the instability of face trackers due to nonlinear and random head motions. To increase the traditional particle filter\u27s sampling efficiency an improved version of the particle filter is introduced that considers the latest measurements. This improved particle filter employs a new colour-based bottom-up approach that leads particles to generate an effective proposal distribution. The colour-based bottom-up approach is a classification technique for fast skin colour segmentation. This method is independent to distribution shape and does not require excessive memory storage or exhaustive prior training. Finally, to address the adaptability of the colour-based face tracker to illumination changes, an original likelihood model is proposed based of spatial rank information that considers both the illumination invariant colour ordering of a face\u27s pixels in an image or video frame and the spatial interaction between them. The original contribution of this work lies in the unique mixture of existing and proposed components to improve colour-base recognition and tracking of faces in complex scenes, especially where drastic illumination changes occur. Experimental results of the final version of the proposed face tracker, which combines the methods developed, are provided in the last chapter of this manuscript
Improved Hand-Tracking Framework with a Recovery Mechanism
AbstractāHand-tracking is fundamental to translating sign language to a spoken language. Accurate and reliable sign language translation depends on effective and accurate hand-tracking. This paper proposes an improved hand-tracking framework that includes a tracking recovery algorithm optimising a previous framework to better handle occlusion. It integrates the tracking recovery algorithm to improve the discrimination between hands and the tracking of hands. The framework was evaluated on 30 South African Sign Language phrases that use: a single hand; both hands without occlusion; and both hands with occlusion. Ten individuals in constrained and unconstrained environments performed the gestures. Overall, the proposed framework achieved an average success rate of 91.8% compared to an average success rate of 81.1% using the previous framework. The results show an improved tracking accuracy across all signs in constrained and unconstrained environments
Towards sociable virtual humans : multimodal recognition of human input and behavior
One of the biggest obstacles for constructing effective sociable virtual humans lies in the failure of machines to recognize the desires, feelings and intentions of the human user. Virtual humans lack the ability to fully understand and decode the communication signals human users emit when communicating with each other. This article describes our research in overcoming this problem by developing senses for the virtual humans which enables them to hear and understand human speech, localize the human user in front of the display system, recognize hand postures and to recognize the emotional state of the human user by classifying facial expression. We report on the methods needed to perform these tasks in real-time and conclude with an outlook on promising research issues of the future
Spatiotemporal visual analysis of human actions
In this dissertation we propose four methods for the recognition of human activities. In all four of
them, the representation of the activities is based on spatiotemporal features that are automatically
detected at areas where there is a significant amount of independent motion, that is, motion that is
due to ongoing activities in the scene. We propose the use of spatiotemporal salient points as features
throughout this dissertation. The algorithms presented, however, can be used with any kind of features,
as long as the latter are well localized and have a well-defined area of support in space and time. We
introduce the utilized spatiotemporal salient points in the first method presented in this dissertation.
By extending previous work on spatial saliency, we measure the variations in the information content of
pixel neighborhoods both in space and time, and detect the points at the locations and scales for which
this information content is locally maximized. In this way, an activity is represented as a collection of
spatiotemporal salient points. We propose an iterative linear space-time warping technique in order
to align the representations in space and time and propose to use Relevance Vector Machines (RVM)
in order to classify each example into an action category. In the second method proposed in this
dissertation we propose to enhance the acquired representations of the first method. More specifically,
we propose to track each detected point in time, and create representations based on sets of trajectories,
where each trajectory expresses how the information engulfed by each salient point evolves over time.
In order to deal with imperfect localization of the detected points, we augment the observation model
of the tracker with background information, acquired using a fully automatic background estimation
algorithm. In this way, the tracker favors solutions that contain a large number of foreground pixels.
In addition, we perform experiments where the tracked templates are localized on specific parts of the
body, like the hands and the head, and we further augment the trackerās observation model using a
human skin color model. Finally, we use a variant of the Longest Common Subsequence algorithm
(LCSS) in order to acquire a similarity measure between the resulting trajectory representations, and
RVMs for classification. In the third method that we propose, we assume that neighboring salient
points follow a similar motion. This is in contrast to the previous method, where each salient point was
tracked independently of its neighbors. More specifically, we propose to extract a novel set of visual
descriptors that are based on geometrical properties of three-dimensional piece-wise polynomials. The
latter are fitted on the spatiotemporal locations of salient points that fall within local spatiotemporal
neighborhoods, and are assumed to follow a similar motion. The extracted descriptors are invariant in
translation and scaling in space-time. Coupling the neighborhood dimensions to the scale at which the
corresponding spatiotemporal salient points are detected ensures the latter. The descriptors that are
extracted across the whole dataset are subsequently clustered in order to create a codebook, which is
used in order to represent the overall motion of the subjects within small temporal windows.Finally,we use boosting in order to select the most discriminative of these windows for each class, and RVMs for
classification. The fourth and last method addresses the joint problem of localization and recognition
of human activities depicted in unsegmented image sequences. Its main contribution is the use of an
implicit representation of the spatiotemporal shape of the activity, which relies on the spatiotemporal
localization of characteristic ensembles of spatiotemporal features. The latter are localized around
automatically detected salient points. Evidence for the spatiotemporal localization of the activity
is accumulated in a probabilistic spatiotemporal voting scheme. During training, we use boosting in
order to create codebooks of characteristic feature ensembles for each class. Subsequently, we construct
class-specific spatiotemporal models, which encode where in space and time each codeword ensemble
appears in the training set. During testing, each activated codeword ensemble casts probabilistic
votes concerning the spatiotemporal localization of the activity, according to the information stored
during training. We use a Mean Shift Mode estimation algorithm in order to extract the most probable
hypotheses from each resulting voting space. Each hypothesis corresponds to a spatiotemporal volume
which potentially engulfs the activity, and is verified by performing action category classification with
an RVM classifier
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
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