text: Automatic video keyword generation is one of the key ingredients in reducing the burden of security officers in analyzing surveillance videos. Keywords or attributes are generally chosen manually based on expert knowledge of surveillance. Most existing works primarily aim at either supervised learning approaches relying on extensive manual labelling or hierarchical probabilistic models that assume the features are extracted using the bag-of-words approach; thus limiting the utilization of the other features. To address this, we turn our attention to automatic attribute discovery approaches. However, it is not clear which automatic discovery approach can discover the most meaningful attributes. Furthermore, little research has been done on how to compare and choose the best automatic attribute discovery methods. In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches.We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets. The evaluation shows that our approach is able to select the automatic discovery approach that discovers the most meaningful attributes. We then employ the best discovery approach to generate keywords for videos recorded from a surveillance system. This work shows it is possible to massively reduce the amount of manual work in generating video keywords without limiting ourselves to a particular video feature descriptor.
  @cite_11: The growing rate of public space closed-circuit television (CCTV) installations has generated a need for automated methods for exploiting video surveillance data, including scene understanding, query, behavior annotation, and summarization. For this reason, extensive research has been performed on surveillance scene understanding and analysis. However, most studies have considered single scenes or groups of adjacent scenes. The semantic similarity between different but related scenes (e.g., many different traffic scenes of a similar layout) is not generally exploited to improve any automated surveillance tasks and reduce manual effort. Exploiting commonality and sharing any supervised annotations between different scenes is, however, challenging due to the following reason: some scenes are totally unrelated and thus any information sharing between them would be detrimental, whereas others may share only a subset of common activities and thus information sharing is only useful if it is selective. Moreover, semantically similar activities that should be modeled together and shared across scenes may have quite different pixel-level appearances in each scene. To address these issues, we develop a new framework for distributed multiple-scene global understanding that clusters surveillance scenes by their ability to explain each other’s behaviors and further discovers which subset of activities are shared versus scene specific within each cluster. We show how to use this structured representation of multiple scenes to improve common surveillance tasks, including scene activity understanding, cross-scene query-by-example, behavior classification with reduced supervised labeling requirements, and video summarization. In each case, we demonstrate how our multiscene model improves on a collection of standard single-scene models and a flat model of all scenes.
summary: To the best of our knowledge, there is only one work that specifically targets automatic video description problems in surveillance videos. Xu al @cite_11 develop a novel distributed multiple-scene global understanding framework that clusters surveillance scenes by their ability to explain each other’s behaviours. However, their work only focuses on multiple-scene case and again, utilizes hierarchical probabilistic models.