  We present a new and challenging object detection dataset, ParkingSticker,
which mimics the type of data available in industry problems more closely than
popular existing datasets like PASCAL VOC. ParkingSticker contains 1,871 images
that come from a security camera's video footage. The objective is to identify
parking stickers on cars approaching a gate that the security camera faces.
Bounding boxes are drawn around parking stickers in the images. The parking
stickers are much smaller on average than the objects in other popular object
detection datasets; this makes ParkingSticker a challenging test for object
detection methods. This dataset also very realistically represents the data
available in many industry problems where a customer presents a few video
frames and asks for a solution to a very difficult problem. Performance of
various object detection pipelines using a YOLOv2 architecture are presented
and indicate that identifying the parking stickers in ParkingSticker is
challenging yet feasible. We believe that this dataset will challenge
researchers to solve a real-world problem with real-world constraints such as
non-ideal camera positioning and small object-size-to-image-size ratios.
