What is Spatiotemporal Video Segmentation

Access to video content, either amateur or professional, is nowadays a key element in business environments, as well as everyday practice for individuals all over the world. The widespread availability of inexpensive video capturing devices, the significant proliferation of broadband Internet connections and the development of innovative video sharing services over the World Wide Web have contributed the most to the establishment of digital video as a necessary part of our lives. However, these developments have also inevitably resulted in a tremendous increase in the amount of video material created every day. This presents new possibilities for businesses and individuals alike. Business opportunities in particular include the development of applications for semantics-based retrieval of video content from the Internet, video stock agencies or personal collections; semantics-aware delivery of video content in desktop and mobile devices; and semantics-based video coding and transmission. Evidently, the above opportunities also reflect to the video manipulation possibilities offered to individual users. Besides opportunities, though, the abundance of digital video content also presents new and important technological challenges, which are crucial for the further development of the aforementioned innovative services. The cornerstone of the efficient manipulation of video material is the understanding of its underlying semantics, a goal that has long been identified as the “Holy grail of content-based media analysis research” (Chang, 2002). Efforts to understand the semantics of video content typically build on algorithms that operate at the signal level, such as temporal and spatiotemporal video segmentation algorithms that aim at partitioning a video stream into semantically meaningful parts. To support the goal of semantic analysis, these signal-level algorithms are augmented with a priori knowledge regarding the different semantic objects and events of interest that may appear in the video and their signal-level properties. The introduction of a priori knowledge serves the purpose of facilitating the detection and exploitation of the hidden associations between the signal and semantic levels, resulting in the generation of semantically meaningful metadata for the video content. In this article, existing state-of-the-art semantic video analysis and understanding techniques are reviewed, including a hybrid approach to semantic video analysis that is outlined in some more detail, and the future trends in this research area are identified. The literature presentation starts in the following section with signal level algorithms for processing video content, a necessary prerequisite for the subsequent application of knowledge-based techniques.