A New Framework for Studying Tubes Rearrangement Strategies in Surveillance Video Synopsis
Giovanna Pappalardo, Dario Allegra, Filippo Stanco, Sebastiano Battiato · 2019
The manual review of raw surveillance video is a time consuming task which can be optimized by using a Video Synopsis (VS) algorithm. The aim of such approaches is to condense a long video into shorter one to allow a quicker review of surveillance data. However, VS is a complex problem. A typical object-based VS algorithm requires three main modules to perform the following tasks: object detection and tracking, tubes rearrangement, condensed video generation. Although the aforementioned three steps are equally critical, we realized that the core of Video Synopsis lies in the tubes rearrangement. This led us to propose an original approach to tackle the problem of tubes rearrangement. To this aim, we first introduce a new toolbox to generate a proper testing dataset, which allows to bypass the lack of public databases including proper annotated videos for testing synopsis approaches. Additionally, we propose an improvement of a tubes arrangement algorithm based on graph colouring and we prove its validity on our generated dataset. For a proper comparison, we show that our algorithm also outperforms the original one on UA-DETRAC public dataset.