Semantic Video Object Based BACnet Visual Surveillance

With the rapid development of video processing and network technology, exploiting visual surveillance information has become an inevitable trend. The surveillance system should be capable of transmitting video data safely, analyzing visual scene, storing and indexing video data by scene information, and integrating with other control systems. All those capabilities rely on processing of semantic video objects in scene. This thesis focuses on semantic video objects segmentation and tracking in surveillance scene, and interoperation based on objects among different systems.Technical requirements for intelligent surveillance system are discussed at first. Then the state-of-the-art of moving object segmentation, tracking and moving shadow detection techniques is reviewed. As communication platform for building control system, BACnet protocol needs interoperation based on surveillance information.Semantic abstract in scene are different for different surveillance applications. Three semantic video objects are defined in this thesis, which are moving object, moving shadow, and moving blob. Every semantic video object's properties including pixel and semantic characteristics constitute semantic descriptors, which represent visual data semantically.Problems like unavailable background pixels, noise and moving objects'velocity in the scene make background estimation more difficult in video surveillance. A similarity-measurement method is provided to reconstruct background. By comparing with temporal blocks and spatial blocks, block similarity measurement helps to valid candidate background blocks. The method deals well with noise and moving object's velocity automatically, and has lower computation cost.This thesis proposed a multi-feature moving shadow detection approach based on albedo ratio similarity region. After analyzing ambient illumination feature and edge information in those similarity regions, moving shadow can be detected from moving video object. The approach is suitable for indoor shadow detection.Semantic interaction based moving object tracking is put forword to deal with occlusion when two objects interact. The approach is based on modeling major color regions of moving human body such as head, torso, and lower limbs blob. These blobs are represented as moving blob descriptions. After projecting those descriptions, moving objects should be refined and validated. Improved fast gauss transform (IFGT) is exploited for semantic video object blob. By choosing target and source number, which used for IFGT, computation cost becomes lower. The tracking approach is simple and available for multi-object tracking.For system interoperation, a new BACnet video object model and video point operation service are proposed at first time. According to this model, a surveillance application scheme based on scene events is built in intelligent building system.In order to put intelligent video surveillance system into building control systems, FIPA-based multi-Agent multi-service integration architecture is put forward. The architecture discussed core framework of agent and request control among agents. It's convenience for consumers to deploy system intelligent.