An abandoned object is one that remains stationary for an extended period. Such object might contain explosives and if left on purpose could cause death and injuries to people especially in crowded places. Abandoned objects need to be detected on time to prevent what might endanger people’s lives and health. Various methods have been developed to detect abandoned objects. The most reliable one is the vision-based method which automatically detects the abandoned object using image processing. The efficiency of the method was tested and evaluated on the customized datasets as well as the i-Lids advanced video surveillance system database. The Self -organizing Background Subtraction (SOBS) method overrides other methods in terms of its detection accuracy and simplicity of implementation, but fails for dynamic background scenarios. This work presents a real time vision-based object detection method using the centroid difference to improve on the accuracy of the detection and to tackle challenges of dynamic background of the SOBS method. Matlab Image processing toolbox was used to achieve this goal. The strategy is basically decomposed into two; foreground detection and stationary foreground object (SFO) detection. Gaussian Mixture Model is used for detecting the presence of newly introduced object into a scene (foreground detection), while the blob tracking approach based on frame counting is used to determine whether the detected foreground object is static/ abandoned or not. The results show that the detection accuracy of 83% was obtained which outperform the SOBS method with 67% accuracy. Future research should focus on tracking the person that abandoned the object for onward prosecution.
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