A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Motion Detection Based on Local Variation of Spatiotemporal Texture
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Object Tracking with Dynamic Template Update and Occlusion Detec
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Comparison of target detection algorithms using adaptive background models
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Generative model for abandoned object detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A distributed architecture for flexible multimedia management and retrieval
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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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).