  Online continual learning from data streams in dynamic environments is a
critical direction in the computer vision field. However, realistic benchmarks
and fundamental studies in this line are still missing. To bridge the gap, we
present a new online continual object detection benchmark with an egocentric
video dataset, Objects Around Krishna (OAK). OAK adopts the KrishnaCAM videos,
an ego-centric video stream collected over nine months by a graduate student.
OAK provides exhaustive bounding box annotations of 80 video snippets (~17.5
hours) for 105 object categories in outdoor scenes. The emergence of new object
categories in our benchmark follows a pattern similar to what a single person
might see in their day-to-day life. The dataset also captures the natural
distribution shifts as the person travels to different places. These egocentric
long-running videos provide a realistic playground for continual learning
algorithms, especially in online embodied settings. We also introduce new
evaluation metrics to evaluate the model performance and catastrophic
forgetting and provide baseline studies for online continual object detection.
We believe this benchmark will pose new exciting challenges for learning from
non-stationary data in continual learning. The OAK dataset and the associated
benchmark are released at https://oakdata.github.io/.
