RETRACTED: Crowd Sensing Based Semantic Annotation of Surveillance Videos

Abstract

Today, video surveillance technology is playing a more and more important role in traffic detection. Vehicle's static properties are crucial information in examining criminal and traffic violations. With the development of video surveillance technology, it has been wildly used in the traffic monitoring. Image and video resources play an important role in traffic events analysis. With the rapid growth of the video surveillance devices, a large number of image and video resources are increasingly being created. It is crucial to explore, share, reuse, and link these multimedia resources for better organizing traffic events. Most of the video resources are currently annotated in an isolated way, which means that they lack semantic connections. Thus, providing the facilities for annotating these video resources is highly demanded. These facilities create the semantic connections among video resources and allow their metadata to be understood globally. Adopting semantic technologies, this paper introduces a video annotation platform. The platform enables user to semantically annotate video resources using vocabularies defined by traffic events ontologies. Moreover, the platform provides the search interface of annotated video resources. The result of initial development demonstrates the benefits of applying semantic technologies in the aspects of reusability, scalability, and extensibility.