Crowd counting: a behavioural economics perspective

Consumer research: Financial Capability: A Behavioural Economics Perspective

Unsupervised Crowd Counting

Most crowd counting methods rely on training with labeled data to learn a mapping between image features and the number of people in the scene. However, the nature of this mapping may change as a function of the scene, camera parameters, illumination etc., limiting the ability of such supervised systems to generalize to novel conditions. Here we propose an alternative, unsupervised strategy. Th...

Counting with the Crowd

In this paper, we address the problem of selectivity estimation in a crowdsourced database. Specifically, we develop several techniques for using workers on a crowdsourcing platform like Amazon’s Mechanical Turk to estimate the fraction of items in a dataset (e.g., a collection of photos) that satisfy some property or predicate (e.g., photos of trees). We do this without explicitly iterating th...

Behavioural Public Economics : Extended

Alós-Ferrer and Granić (2012) present results from two *eld experiments on electoral reform, which were carried out alongside o-cial state and federal elections in Germany, in 2008 and 2009, respectively. Electors who had voted in the o-cial elections, which used the mixed-member proportional representation (MMPR) system, were given the opportunity to vote in an experimental election using the ...

Depth Information Guided Crowd Counting for Complex Crowd Scenes

It is important to monitor and analyze crowd events for the sake of city safety. In an EDOF (extended depth of field) image with a crowded scene, the distribution of people is highly imbalanced. People far away from the camera look much smaller and often occlude each other heavily, while people close to the camera look larger. In such a case, it is difficult to accurately estimate the number of...