Digital image processing: principles and applications
Digital image processing: principles and applications
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Evaluation of MPEG7 color descriptors for visual surveillance retrieval
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Motion prediction of moving objects based on autoregressive model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Embedding Motion in Model-Based Stochastic Tracking
IEEE Transactions on Image Processing
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Camera based supervision is a critical component for patient monitoring in assistive environments. However, visual tracking still remains one of the biggest challenges in the area computer vision although it has been extensively studied during the previous decades. It this paper we propose a hybrid Rao -- Blackwellzed particle filter that combines two efficient, well-known tracking techniques with an innovative color observation representation method in order to improve the overall tracking performance. This representation is combined with color and edge representation to obtain improved tracking efficiency. Furthermore, the global edge description template for the edge representation (histogram of oriented gradients) was obtained using a machine learning technique. Initial experiments show that the principle behind the proposed algorithm is sound, yielding good results and thus allowing its adoption as an initial stage for patient behavior recognition.