Thingscoop is a tool for analyzing videos semantically - that means searching, filtering, and describing videos based on objects and places that appear in them. Thingscoop is based on Caffe, an open-source deep learning framework.
When you first run thingscoop on a video file, it uses a convolutional neural network to create an "index" of what's contained in the every second of the input video by repeatedly performing image classification on its frames. Once an index for a video file has been created, you can search (i.e. get the start and end times of the regions in the video matching the query) and filter (i.e. create a supercut of the matching regions) the input using arbitrary queries.
Thingscoop uses a very basic query language that lets you to compose queries that test for the presence or absence of labels with the logical operators ! (not), || (or) and && (and). For example, to search a video for the presence of the sky and the absence of the ocean: thingscoop search 'sky && !ocean'.