The ZMET Technique: A New Paradigm for Improving Marketing and Marketing Research

For decades, marketing and marketing research have been based on a concept of consumer behaviour that is deeply embedded in a linear notion of marketing activities. With increasing regularity, key organising frameworks for marketing and marketing activities are being challenged by academics and practitioners alike. In turn, this has led to the search for new approaches and tools that will help marketers understand the interaction among attitudes, emotions and product/brand choice. More recently, the approach developed by Harvard Professor, Gerald Zaltman, referred to as the Zaltman Metaphor Elicitation Technique (ZMET) has gained considerable interest. This paper seeks to demonstrate the effectiveness of this alternative qualitative method, using a non-conventional approach, thus providing a useful contribution to the qualitative research area.

Adaptive optical flow for person tracking

Person tracking systems are dependent on being able to locate a person accurately across a series of frames. Optical flow can be used to segment a moving object from a scene, provided the expected velocity of the moving object is known; but successful detection also relies on being able segment the background. A problem with existing optical flow techniques is that they don’t discriminate the foreground from the background, and so often detect motion (and thus the object) in the background. To overcome this problem, we propose a new optical flow technique, that is based upon an adaptive background segmentation technique, which only determines optical flow in regions of motion. This technique has been developed with a view to being used in surveillance systems, and our testing shows that for this application it is more effective than other standard optical flow techniques.