KrishnaCam: Using a longitudinal, single-person, egocentric dataset for scene understanding tasks

Krishna Kumar Singh, Kayvon Fatahalian, Alexei A. Efros
2016 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)  
We record, and analyze, and present to the community, KrishnaCam, a large (7.6 million frames, 70 hours) egocentric video stream along with GPS position, acceleration and body orientation data spanning nine months of the life of a computer vision graduate student. We explore and exploit the inherent redundancies in this rich visual data stream to answer simple scene understanding questions such as: How much novel visual information does the student see each day? Given a single egocentric
more » ... aph of a scene, can we predict where the student might walk next? We find that given our large video database, simple, nearest-neighbor methods are surprisingly adept baselines for these tasks, even in scenes and scenarios where the camera wearer has never been before. For example, we demonstrate the ability to predict the near-future trajectory of the student in broad set of outdoor situations that includes following sidewalks, stopping to wait for a bus, taking a daily path to work, and the lack of movement while eating food.
doi:10.1109/wacv.2016.7477717 dblp:conf/wacv/SinghFE16 fatcat:aua4mbe5cjfcrclace6qy4xi44