Person tracking using motion detection and optical flow

Person tracking systems to date have either relied on motion detection or optical flow as a basis for person detection and tracking. As yet, systems have not been developed that utilise both these techniques. We propose a person tracking system that uses both, made possible by a novel hybrid optical flow-motion detection technique that we have developed. This provides the system with two methods of person detection, helping to avoid missed detections and the need to predict position, which can lead to errors in tracking and mistakes when handling occlusion situations. Our results show that our system is able to track people accurately, with an average error less than four pixels, and that our system outperforms the current CAVIAR benchmark system.

Testing a framework for the quality of process models: A case study

Process modeling can be regarded as the currently most popular form of conceptual modeling. Research evidence illustrates how process modeling is applied across the different information system life cycle phases for a range of different applications, such as configuration of Enterprise Systems, workflow management, or software development. However, a detailed discussion of critical factors of the quality of process models is still missing. This paper proposes a framework consisting of six quality factors, which is derived from a comprehensive literature review. It then presents in a case study, a utility provider, who had designed various business process models for the selection of an Enterprise System. The paper summarizes potential means of conducting a successful process modeling initiative and evaluates the described modeling approach within the Guidelines of Modeling (GoM) framework. An outlook shows the potential lessons learnt, and concludes with insights to the next phases of this study.