Coding behavioral video is an important method used by researchers to understand social phenomenon. Unfortunately, traditional hand-coding approaches can take days or weeks of time to complete. Recent work has shown that these tasks can be completed quickly by leveraging the parallelism of large online crowds, but using the crowd introduces new concerns about accuracy, reliability, privacy, and cost. To explore these issues, we conducted interviews with 12 researchers who frequently code behavioral video, to investigate common practices and challenges with video coding. We find accuracy and privacy to be the researchers' primary concerns. To explore this more concretely, we used sample videos to investigate whether crowds can accurately recognize instances of commonly coded behaviors, and show that the crowd yields accurate results. Then, we demonstrate a method for obfuscating participant identity with a video blur filter, and find, as expected, that workers' ability to identify participants decreases as blur level increases. The workers' ability to accurately and reliably code behaviors also decreases, but not as steeply as the identity test. This trade-off between coding quality and privacy protection suggests that researchers can use online crowds to code for some key behaviors in video without compromising participant identity. We conclude with a discussion of how researchers can balance privacy and accuracy on their own data using a system we introduce called Incognito.

Behavioral researchers code video to extract systematic meaning from subtle human actions and emotions. While this has traditionally been done by analysts within a research group, recent methods have leveraged online crowds to massively parallelize this task and reduce the time required from days to seconds. However, using the crowd to code video increases the risk that private information will be disclosed because workers who have not been vetted will view the video data in order to code it. In this Work-in-Progress, we discuss techniques for maintaining privacy when using Glance to code video and present initial experimental evidence to support them.

The ability to predict rover locomotion performance is critical during the design, validation and operational phases of a planetary robotic mission. Predicting locomotion performance depends on the ability to accurately characterize the wheel-soil interactions. In this research, wheel-soil interaction experiments were carried out on a single-wheel testbed and the results were compared with a single-wheel dynamic computer simulator which was developed in Matlab and Simulink's SimMechanics toolbox using a commercially-available wheel-soil interaction computer model called AESCO Soft Soil Tire Model (AS/sup 2/TM). Two different tire treads were used and compared in this study. There is good agreement between experimental and simulation results for wheel sinkage as a function of slip ratio; however, more investigation is needed to understand the differences observed for the drawbar pull and motor torque results.