'You run out of hope': an exploration of low-income parents' experiences with food insecurity using Photovoice.

OBJECTIVE: Using an adaption of the Photovoice method, this study explored how food insecurity affected parents' ability to provide food for their family, their strategies for managing household food insecurity and the impact of food insecurity on their well-being. DESIGN: Parents submitted photos around their families' experiences with food insecurity. Afterwards, they completed in-depth, semi-structured interviews about their photos. The interviews were transcribed and analysed for thematic content using the constant comparative method. SETTING: San Francisco Bay Area, California, USA. PARTICIPANTS: Seventeen parents (fourteen mothers and three fathers) were recruited from a broader qualitative study on understanding the experiences of food insecurity in low-income families. RESULTS: Four themes were identified from the parents' photos and interviews. First, parents described multiple aspects of their food environment that promoted unhealthy eating behaviours. Second, parents shared strategies they employed to acquire food with limited resources. Third, parents expressed feelings of shame, guilt and distress resulting from their experience of food insecurity. And finally, parents described treating their children to special foods to cultivate a sense of normalcy. CONCLUSIONS: Parents highlighted the external contributors and internal struggles of their experiences of food insecurity. Additional research to understand the experiences of the food-insecure families may help to improve nutrition interventions targeting this structurally vulnerable population.

Multi-View Video Synopsis via Simultaneous Object-Shifting and View-Switching Optimization.

We present a method for synopsizing multiple videos captured by a set of surveillance cameras with some overlapped field-of-views. Currently, object-based approaches that directly shift objects along the time axis are already able to compute compact synopsis results for multiple surveillance videos. The challenge is how to present the multiple synopsis results in a more compact and understandable way. Previous approaches show them side by side on the screen, which however is difficult for user to comprehend. In this paper, we solve the problem by joint object-shifting and camera view-switching. Firstly, we synchronize the input videos, and group the same object in different videos together. Then we shift the groups of objects along the time axis to obtain multiple synopsis videos. Instead of showing them simultaneously, we just show one of them at each time, and allow to switch among the views of different synopsis videos. In this view switching way, we obtain just a single synopsis results consisting of content from all the input videos, which is much easier for user to follow and understand. To obtain the best synopsis result, we construct a simultaneous object-shifting and view-switching optimization framework instead of solving them separately. We also present an alternative optimization strategy composed of graph cuts and dynamic programming to solve the unified optimization. Experiments demonstrate that our single synopsis video generated from multiple input videos is compact, complete, and easy to understand.