Understanding Traffic Density from Large-Scale Web Camera Data

Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective. To deeply understand traffic density, we explore both deep learning based and optimization based methods. To avoid individual vehicle detection and tracking, both methods map the image into vehicle density map, one based on rank constrained regression and the other one based on fully convolution networks (FCN). The regression based method learns different weights for different blocks in the image to increase freedom degrees of weights and embed perspective information. The FCN based method jointly estimates vehicle density map and vehicle count with a residual learning framework to perform end-to-end dense prediction, allowing arbitrary image resolution, and adapting to different vehicle scales and perspectives. We analyze and compare both methods, and get insights from optimization based method to improve deep model. Since existing datasets do not cover all the challenges in our work, we collected and labelled a large-scale traffic video dataset, containing 60 million frames from 212 webcams. Both methods are extensively evaluated and compared on different counting tasks and datasets. FCN based method significantly reduces the mean absolute error from 10.99 to 5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data

Capturing and representing deliberation in participatory planning practices

In this paper we argue for the importance of capturing and representing deliberation in participatory planning practices. We discuss the concept of deliberation in planning theory, and argue for a paradigm that puts deliberation at the centre of public participation to planning decision. We argue that in order to enable effective participation, the normally ephemeral delib- eration process needs to be captured and represented so that the information and knowledge gathered during deliberation is visible for all, can be effectively traced, reused, and can actively influence planning decisions. To scaffold this we describe the integration of three technologies to create a collective project memory structured against five dimensions of participatory plan- ning processes: dialogical, social, spatial, temporal and causal. Based on several authentic par- ticipatory planning cases, we report that this supported deliberation across planning tasks, communication modes, time and environments. The coupled use of online and offline group- ware technologies created a more expressive and transparent participatory knowledge base than is possible with conventional media, and enhanced participatory planning by: supporting the effective capture and representation of deliberation processes and products; providing a rich picture of the social setting in which planning decision develops and supporting reflection in and on planning actions

Family memories in the home: contrasting physical and digital mementos

We carried out fieldwork to characterise and compare physical and digital mementos in the home. Physical mementos are highly valued, heterogeneous and support different types of recollection. Contrary to expectations, we found physical mementos are not purely representational, and can involve appropriating common objects and more idiosyncratic forms. In contrast, digital mementos were initially perceived as less valuable, although participants later reconsidered this. Digital mementos were somewhat limited in function and expression, largely involving representational photos and videos, and infrequently accessed. We explain these digital limitations and conclude with design guidelines for digital mementos, including better techniques for accessing and integrating these into everyday life, allowing them to acquire the symbolic associations and lasting value that characterise their physical counterparts