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Consumer video camera surveillance with the continuous advancements of image processing technologies is emerging for consumer world of applications. Technology for detecting objects left unattended in consumer world such as shopping malls, airports, railways stations has resulted in successful commercialization, worldwide sales and the winning of international awards. However, as a consumer video application the need is now greater than ever for a surveillance system that is robustly and effectively automated. In this paper, we propose an intelligent vision based analyzer for semantic analysis of objects left unattended relation with human behaviors from a monocular surveillance video, captured by a consumer camera through cluttered environments. Our analyzer employs visual cues to robustly and efficiently detect unattended objects which are usually considered as potential security breach in public safety from terrorist explosive attacks. The proposed system consists of three processing steps: (i) object extraction, involving a new background subtraction algorithm based on combination of periodic background models with shadow removal and quick lighting change adaptation,(ii) extracted objects classification as stationary or dynamic objects, and (iii) classified objects investigation by using running average about the static foreground masks to calculate a confidence score for the decision making about event (either unattended or very still person). We show attractive experimental results, highlighting the system efficiency and classification capability by using our real-time consumer video surveillance system for public safety application in big cities.