Detecting Social Actions of Fruit Flies

Eyrun Eyjolfsdottir, Steve Branson, Xavier P. Burgos-Artizzu, Eric D. Hoopfer, Jonathan Schor, David J. Anderson, Pietro Perona

Abstract: We describe a system that tracks pairs of fruit flies and automatically detects and classifies their actions. We compare experimentally the value of a frame-level feature representation with the more elaborate notion of ‘bout features’ that capture the structure within actions. Similarly, we compare a simple sliding window classifier architecture with a more sophisticated structured output architecture, and find that window based detectors outperform the much slower structured counterparts, and approach human performance. In addition we test our top performing detector on the CRIM13 mouse dataset, finding that it matches the performance of the best published method. Our Fly-vs-Fly dataset contains 22 hours of video showing pairs of fruit flies engaging in 10 social interactions in three different contexts; it is fully annotated by experts, and published with articulated pose trajectory features.