CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Fast algorithms for hierarchical range histogram construction
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Performance of similarity measures based on histograms of local image feature vectors
Pattern Recognition Letters
A spatial-color mean-shift object tracking algorithm with scale and orientation estimation
Pattern Recognition Letters
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
A histogram modification framework and its application for image contrast enhancement
IEEE Transactions on Image Processing
An incremental Bhattacharyya dissimilarity measure for particle filtering
Pattern Recognition
Adaptive Object Tracking Based on an Effective Appearance Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape preserving local histogram modification
IEEE Transactions on Image Processing
Median Filtering in Constant Time
IEEE Transactions on Image Processing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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In this paper we present a new method for fast histogram computing and its extension to bin to bin histogram distance computing. The idea consists in using the information of spatial differences between images, or between regions of images (a current one and a reference one), and encoding it into a specific data structure: a tree. The histogram of the current image or of one of its regions is then computed by updating the histogram of the reference one using the temporal data stocked into the tree. With this approach, we never need to store any of the current histograms, except the reference image ones, as a preprocessing step. We compare our approach with the well-known Integral Histogram one, and obtain better results in terms of processing time while reducing the memory footprint. We show theoretically and with experimental results the superiority of our approach in many cases. We also extend our idea to the computation of the Bhattacharyya distance between two histograms, using a similar incremental approach that also avoid current histogram computations: we just need histograms of the reference image, and spatial differences between the reference and the current image to compute this distance using an updating process. Finally, we demonstrate the advantages of our approach on a real visual tracking application using a particle filter framework by improving its correction step computation time.