The design of a wireless real-time visual surveillance system

Abstract

In this paper, we study the important issues in the design of an efficient wireless real-time visual surveillance system (WISES). Two important considerations are to minimize: (1) the video workload on the wireless network; and (2) the processing workload at the front-end video capturing unit. To achieve the first objective, we propose a cooperative framework for semantic filtering of video frames instead of forwarding every video frame to the back-end server for analysis and monitoring query evaluation. To minimize the processing workload at the front-end unit, a hierarchical object model (HOM) is designed to model the status of the objects, and their temporal and spatial properties in the video scene. With the information provided from the back-end server, the front-end unit pre-analyses the current status of the objects in the HOM by comparing the selection conditions in the submitted monitoring queries following the adaptive object-based evaluation (APOBE) scheme which is proposed to reduce the processing workload at the front-end unit. In APOBE, a higher evaluation frequency is given to the object which is closer to satisfy the condition in the monitoring queries. The performance of WISES has been studied to demonstrate the efficiency of the proposed scheme.