In this paper we describe a citizen science system for solving time-consuming and labor-intensive problems, using crowdsourcing and efficient geometric algorithms. Specifically, the system can be used to trace static objects in images (such as trees in an urban environment), or to generate trajectories of moving objects in videos (such as ants in an ant colony). The traces of the static objects can provide quantitative measurements such as size, shape and appearance, for example in monitoring the health of the trees in New York City’s Million Trees Initiative. It is relatively easy to plant a million trees, but ensuring they are healthy and taken care of is a challenge on a different scale, and a challenge where citizen scientists can make a big difference. The ant trajectories extracted from videos of ant colonies are needed by biologists studying longitudinal behavioral patterns in insect colonies. Existing automated solutions are not good enough, and there is only so much data that even motivated students can annotate in the research lab.

AngryAnts is our on-line application which displays short video segments, specifies which ant needs to be traced and allows the citizen scientist to enter the trajectory in a first-person shooter style via mouse clicks. Submitted trajectories are verified using a ReCaptcha method, where part of the trajectory is known to the system and is used as a test of the submission. When we have collected enough traces of a trajectory from citizen scientists we extract an average trajectory using two approaches: local and global. In the local approach we find a representative trajectory for each ant x by considering only the input trajectories for that ant. The representative trajectory is computed using Fr�chet average and median trajectory. We compare the efficiency of our approach with an existing automated ant tracking system. This approach shares a lot in common with the static image case. However, in the dynamic video setting, the local approach may be influenced by mistakes (at some points, some trajectories follow the wrong ants), and by not using possibly useful data (some trajectories for other ants may contain valid pieces for this ant).