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

Prediction of intent in robotics and multi-agent systems.

Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches

The Role-Based Performance Scale: Validity Analysis of a Theory-Based Measure

This study introduces a theory-based measure of employee performance (Role Based Performance Scale, RBPS) that is supported with results from a validation study using 10 data sets from six companies. In contrast to traditional, job-related measures of employee performance, we propose an alternative measure of performance based on role theory and identity theory. Because our results support the validity of the scale, we think that the instrument can be used for future research that requires a generalizable measure of performance. The scale demonstrates diagnostic properties that make it useful for practitioners as well as researchers

Diversifying academic and professional identities in higher education: some management challenges

This paper draws on an international study of the management challenges arising from diversifying academic and professional identities in higher education. These challenges include, for instance, the introduction of practice-based disciplines with different traditions such as health and social care, the changing aspirations and expectations of younger generations of staff, a diffusion of management responsibilities and structures, and imperatives for a more holistic approach to the "employment package", including new forms of recognition and reward. It is suggested that while academic and professional identities have become increasingly dynamic and multi-faceted, change is occurring at different rates in different contexts. A model is offered, therefore, that relates approaches to "people management" to different organisational environments, against the general background of increasing resource constraint arising from the global economic downturn

CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network -- CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. The evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches.Comment: Accepted to IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201