Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not change clothes during the recognition period. This paper provides an outlook on long-term person reidentification, an emerging research topic regarding when consecutive acquisitions of an individual can be found apart for days or even months, making such a task even more challenging. A long-term reidentification system using face recognition is presented to emphasize current techniques’ limitations.

Applying the correct set of Boolean operations is a fundamental task in constructive solid geometry (CSG), which is a staple in automated manufacturing systems. Although textual buttons and icons are the most common interfaces to apply such operations, these require an unnecessary cognitive load that hampers the solid modeling process. This study presents VennPad, a novel CSG widget that gathers all Boolean operations under the same user interface control element and is represented as a two-set Venn diagram. Contrary to conventional CSG widgets, VennPad supports a graphical interface that gives simultaneous access to several types of Boolean operations (intersection, union, difference, symmetric difference and split). A usability study was conducted to ascertain whether VennPad is a more natural interface compared to textual buttons and icon-based widgets for different solid modeling tasks. VennPad proved to be an effective interface to perform Boolean operations. Qualitative feedback places VennPad as the preferred interface, but efficiency results are operation dependent, thus, opening the way to new design iterations.