Knowledge Sharing within IT Projects

Even though today’s corporations recognize that they need to understand modern project management techniques (Schwalbe, 2002, p2), many researchers continue to provide evidence of poor IT project success. With Kotnour, (2000) finding that project performance is positively associated with project knowledge, a better understanding of how to effectively manage knowledge in IT projects should have considerable practical significance for increasing the chances of project success. Using a combined qualitative/quantitative method of data collection in multiple case studies spanning four continents, and comprising a variety of organizational types, the focus of this current research centered on the question of why individuals working within IT project teams might be motivated towards, or inhibited from, sharing their knowledge and experience in their activities, procedures, and processes. The research concluded with the development of a new theoretical model of knowledge sharing behavior, ‘The Alignment Model of Motivational Focus’. This model suggests that an individual’s propensity to share knowledge and experience is a function of perceived personal benefits and costs associated with the activity, balanced against the individual’s alignment to a group of ‘institutional’ factors. These factors are identified as alignments to the project team, to the organization, and dependent on the circumstances, to either the professional discipline or community of practice, to which the individual belongs.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.