{"content":[{"text":"Space-Time Tubes and Motion","bbox":{"left":0.150373,"top":0.079667,"width":0.45660900000000004,"height":0.028041999999999997}},{"text":"Representation","bbox":{"left":0.626678,"top":0.079667,"width":0.220989,"height":0.028041999999999997}},{"text":"Christos Diou, Anastasia Manta, and Anastasios","bbox":{"left":0.220033,"top":0.13532,"width":0.44878399999999996,"height":0.018027000000000015}},{"text":"Delopoulos","bbox":{"left":0.679287,"top":0.13532,"width":0.10063599999999995,"height":0.018027000000000015}},{"text":"Multimedia Understanding Group, \n Department of Electrical and Computer Engineering, \n Aristotle University of Thessaloniki - Greece \n diouiolympus.ee.auth.gr, mantaQauth.gr, adeloieng.auth.gr","bbox":{"left":0.214047,"top":0.164329,"width":0.570766,"height":0.06379200000000002}},{"text":"Abstract. Space-time tubes, a feature that can be used for analysis of \n motion based on the observed moving points in a scene is introduced. \n Information provided by sensors is \\ised to detect moving points and \n based on their connectivity, tubes enable a structured approach towards \n identifying moving objects and high level events. It is shown that using \n tubes in conjunction with domain knowledge can overcome errors caused \n by the inaccuracy or inadequacy of the original motion information. The \n detected high level events can then be mapped to small natural language \n descriptions of object motion in the scene.","bbox":{"left":0.196088,"top":0.253158,"width":0.6079399999999999,"height":0.139108}},{"text":"1 Introduction \n While video motion analysis is a broad subject that has been extensively stud- \n ied, most of the established approaches appear to be insuflScient when it comes \n to semantic analysis of video data. They either provide low level information \n that is primarily useful for coding purposes, or are highly dependent on image \n processing results that lack the accuracy required for identification of natural \n objects or events. Mobile object detection and tracking techniques have demon- \n strated satisfactory results (e.g., [5]), but often heavily rely on the robustness \n and effectiveness of the image processing algorithms applied; the use of common \n sense rules and domain knowledge is usually limited and impHcit, integrated in \n the tracking algorithm. \n In this paper we introduce space-time tubes as a general concept and discuss \n the ways they can be used to identify high level events related to natural object \n motion. One of the most important benefits is that low level processing is aban- \n doned early in the event detection process, while results are mainly obtained \n using reasoning that can accomodate domain knowledge. \n Since tubes have certain properties that can be directly mapped to events \n such as \"Two objects meet\", it is also possible to construct simple nattiral \n langugage descriptions of the events detected in a scene (see section 3).","bbox":{"left":0.135407,"top":0.418847,"width":0.7275929999999999,"height":0.33269699999999996}},{"text":"2 Space-Time Tubes \n Assume that sensors detect motion in a scene, so that \n Ib{xj y, t) is provided:","bbox":{"left":0.134863,"top":0.776324,"width":0.5075989999999999,"height":0.06729300000000005}},{"text":"a binajry motion mask","bbox":{"left":0.654317,"top":0.808653,"width":0.2072259999999999,"height":0.018027000000000015}},{"text":"Please use the following format when citing this chapter: \n Diou, Christos, Manta, Anastasia, Delopoulos, Anastasios, 2006, in IFIP International Federation for \n Information Processing, Volume 204, Artificial Intelligence Applications and Innovations, eds. \n Maglogiannis, I., Karpouzis, K., Bramer, M., (Boston: Springer), pp. 583-590","bbox":{"left":0.134863,"top":0.88591,"width":0.7315879999999999,"height":0.05728500000000003}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}