Detecting and recognizing abandoned objects in crowded environments

Abstract

In this paper we present a framework for detecting and recognizing abandoned objects in crowded environments. The two main components of the framework include background change detection and object recognition. Moving blocks are detected using dynamic thresholding of spatiotemporal texture changes. The background change detection is based on analyzing wavelet transform coefficients of non-overlapping and non-moving 3D texture blocks. Detected changed background becomes the region of interest which is scanned to recognize various objects under surveillance such as abandoned luggage. The object recognition is based on model histogram ratios of image gradient magnitude patches. Supervised learning of the objects is performed by support vector machine. Experimental results are demonstrated using various benchmark video sequences (PETS, CAVIAR, i-Lids) and an object category dataset (CalTech256). © 2008 Springer-Verlag Berlin Heidelberg.