Construction Worker Detection in Video Frames for Initializing Vision Trackers

Monitoring the location of resources on large scale, congested, outdoor sites can be performed more efficiently with vision tracking, as this approach does not require any pre-tagging of resources. However, the greatest impediment to the use of vision tracking in this case is the lack of detection methods that are needed to automatically mark the resources of interest and initiate the tracking. This paper presents such a novel method for construction worker detection that localizes construction workers in video frames. The proposed method exploits motion, shape, and color cues to narrow down the detection regions to moving objects, people, and finally construction workers, respectively. The three cues are characterized by using background subtraction, the histogram of oriented gradients (HOG), and the HSV color histogram. The method has been tested on videos taken in various environments. The results demonstrate its suitability for automatic initialization of vision trackers.

The night purging of a two-storey atrium building

We examine the fluid mechanics of night purging in a two-storey naturally ventilated atrium building. We develop a mathematical model of a simplified atrium building and focus on the rate at which warm air purges from each storey and the atrium by displacement ventilation into a still cool night environment of a constant temperature. To develop a first insight into how the geometry of the building influences the rate at which warm air purges from each storey via the atrium we neglect heat exchange with the fabric (so there is no thermal buffering) and furthermore assume that the warm air layers in each storey and the atrium are of uniform temperature. The plumes of warm air that rise from the storeys into the atrium, causing the atrium to fill with warm air, have a very strong influence on the night purge. Modelling these as axisymmetric turbulent plumes, we identify three forms of purging behaviour. Each purge is characterised by five key times identified in the progression of the night purge and physical rationale for these differing behaviours is given. An interface velocity deficit and volumetric purge deficit are introduced as measures of the efficiency of a night purge. © 2010 Elsevier Ltd.

Evidence-informed development of engineering practical work: Effecting change through staff and student engagement

This paper discusses innovations in curriculum development in the Department of Engineering at the University of Cambridge as a participant in the Teaching for Learning Network (TFLN), a teaching and learning development initiative funded by the Cambridge-MIT Institute a pedagogic collaboration and brokerage network. A year-long research and development project investigated the practical experiences through which students traditionally explore engineering disciplines, apply and extend the knowledge gained in lectures and other settings, and begin to develop their professional expertise. The research project evaluated current practice in these sessions and developed an evidence-base to identify requirements for new activities, student support and staff development. The evidence collected included a novel student 'practice-value' survey highlighting effective practice and areas of concern, classroom observation of practicals, semi-structured interviews with staff, a student focus group and informal discussions with staff. Analysis of the data identified three potentially 'high-leverage' strategies for improvement: development of a more integrated teaching framework, within which practical work could be contextualised in relation to other learning; a more transparent and integrated conceptual framework where theory and practice were more closely linked; development of practical work more reflective of the complex problems facing professional engineers. This paper sets out key elements of the evidence collected and the changes that have been informed by this evidence and analysis, leading to the creation of a suite of integrated practical sessions carefully linked to other course elements and reinforcing central concepts in engineering, accompanied by a training and support programme for teaching staff.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.