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A deep generic to specific recognition model for group membership analysis using non-verbal cues
Automatic understanding and analysis of groups has attracted increasing attention
in the vision and multimedia communities in recent years. However,
little attention has been paid to the automatic analysis of the non-verbal behaviors
and how this can be utilized for analysis of group membership, i.e.,
recognizing which group each individual is part of. This paper presents a
novel Support Vector Machine (SVM) based Deep Specific Recognition Model
(DeepSRM) that is learned based on a generic recognition model. The generic
recognition model refers to the model trained with data across different conditions,
i.e., when people are watching movies of different types. Although the
generic recognition model can provide a baseline for the recognition model
trained for each specific condition, the different behaviors people exhibit in
different conditions limit the recognition performance of the generic model.
Therefore, the specific recognition model is proposed for each condition separately
and built on the top of the generic recognition model. We conduct a set
of experiments using a database collected to study group analysis while each
group (i.e., four participants together) were watching a number of long movie
segments. The proposed deep specific recognition model (44%) outperforms the generic recognition model (26%). The recognition of group membership also indicates that the non-verbal behaviors of individuals within a group share commonalities
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
We present a probabilistic generative model for inferring a description of
coordinated, recursively structured group activities at multiple levels of
temporal granularity based on observations of individuals' trajectories. The
model accommodates: (1) hierarchically structured groups, (2) activities that
are temporally and compositionally recursive, (3) component roles assigning
different subactivity dynamics to subgroups of participants, and (4) a
nonparametric Gaussian Process model of trajectories. We present an MCMC
sampling framework for performing joint inference over recursive activity
descriptions and assignment of trajectories to groups, integrating out
continuous parameters. We demonstrate the model's expressive power in several
simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event
video data sets.Comment: 10 pages, 6 figures, in Proceedings of the 30th AAAI Conference on
Artificial Intelligence (AAAI'16), Phoenix, AZ, 201
Activity-driven content adaptation for effective video summarisation
In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided
Personalizing gesture recognition using hierarchical bayesian neural networks
Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific variations and share statistical strength across subjects. Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy. We also develop methods for adapting our model to new subjects when a small number of subject-specific personalization data is available. Finally, we investigate active learning algorithms for interactively labeling personalization data in resource-constrained scenarios. Focusing on the problem of gesture recognition where inter-subject variations are commonplace, we demonstrate the effectiveness of our proposed techniques. We test our framework on three widely used gesture recognition datasets, achieving personalization performance competitive with the state-of-the-art.http://openaccess.thecvf.com/content_cvpr_2017/html/Joshi_Personalizing_Gesture_Recognition_CVPR_2017_paper.htmlhttp://openaccess.thecvf.com/content_cvpr_2017/html/Joshi_Personalizing_Gesture_Recognition_CVPR_2017_paper.htmlhttp://openaccess.thecvf.com/content_cvpr_2017/html/Joshi_Personalizing_Gesture_Recognition_CVPR_2017_paper.htmlPublished versio
Data fluidity in DARIAH -- pushing the agenda forward
This paper provides both an update concerning the setting up of the European
DARIAH infrastructure and a series of strong action lines related to the
development of a data centred strategy for the humanities in the coming years.
In particular we tackle various aspect of data management: data hosting, the
setting up of a DARIAH seal of approval, the establishment of a charter between
cultural heritage institutions and scholars and finally a specific view on
certification mechanisms for data
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