A framework for suspicious object detection from surveillance video
by Rajesh Kumar Tripathi; Anand Singh Jalal
International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), Vol. 1, No. 3, 2014

Abstract: Detection of suspicious objects from a surveillance video is an important and challenging task. This paper presents a method to detect a suspicious object from surveillance video. It consists of three main steps. In the first step, background subtraction is performed through background modelling using a running average method which makes the system efficient to detect foreground objects. In the second step, static objects are detected from foreground frames through contour features and geometric histogram of an object. In the third step, static objects are classified into human and non-human objects using skin colour region detection and edge-based object recognition method. Edge-based object recognition method is efficient to recognise full and partially visible object. If static object is a non-human, an alarm is raised after a specified time. Experimental results have been performed on the IEEE dataset for Performance Evaluation of Tracking and Surveillance (PETS) and own dataset. The results demonstrate that proposed system is suitable for real-time surveillance video with detection accuracy of 90.9%.

Online publication date: Wed, 31-Dec-2014

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