Summarisation of surveillance videos by key-frame selection

We propose two novel techniques for automatic summarisation of lengthy surveillance videos, based on selection of frames containing scenes most informative for rapid perusal and interpretation by humans. In contrast to other video summarisation methods, the proposed methods explicitly focus on foreground objects, via edge histogram descriptor and a localised foreground information quantity (entropy) measurement. Frames are iteratively pruned until a preset summarisation rate is reached. Experiments on the publicly available CAVIAR dataset, as well as our own dataset focused on people walking through natural choke points (such as doors), suggest that the proposed method obtains considerably better results than methods based on optical flow, entropy differences and colour spatial distribution characteristics.