Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

Does video feedback analysis improve CPR performance in phase 5 medical students?

Background The use of simulation in medical education is increasing, with students taught and assessed using simulated patients and manikins. Medical students at Queen’s University of Belfast are taught advanced life support cardiopulmonary resuscitation as part of the undergraduate curriculum. Teaching and feedback in these skills have been developed in Queen’s University with high-fidelity manikins. This study aimed to evaluate the effectiveness of video compared to verbal feedback in assessment of student cardiopulmonary resuscitation performance Methods Final year students participated in this study using a high-fidelity manikin, in the Clinical Skills Centre, Queen’s University Belfast. Cohort A received verbal feedback only on their performance and cohort B received video feedback only. Video analysis using ‘StudioCode’ software was distributed to students. Each group returned for a second scenario and evaluation 4 weeks later. An assessment tool was created for performance assessment, which included individual skill and global score evaluation. Results One hundred thirty eight final year medical students completed the study. 62 % were female and the mean age was 23.9 years. Students having video feedback had significantly greater improvement in overall scores compared to those receiving verbal feedback (p = 0.006, 95 % CI: 2.8–15.8). Individual skills, including ventilation quality and global score were significantly better with video feedback (p = 0.002 and p < 0.001, respectively) when compared with cohort A. There was a positive change in overall score for cohort B from session one to session two (p < 0.001, 95 % CI: 6.3–15.8) indicating video feedback significantly benefited skill retention. In addition, using video feedback showed a significant improvement in the global score (p < 0.001, 95 % CI: 3.3–7.2) and drug administration timing (p = 0.004, 95 % CI: 0.7–3.8) of cohort B participants, from session one to session two. Conclusions There is increased use of simulation in medicine but a paucity of published data comparing feedback methods in cardiopulmonary resuscitation training. Our study shows the use of video feedback when teaching cardiopulmonary resuscitation is more effective than verbal feedback, and enhances skill retention. This is one of the first studies to demonstrate the benefit of video feedback in cardiopulmonary resuscitation teaching.