A technical introduction to digital video
A technical introduction to digital video
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
Taxonomy of nominal type histogram distance measures
MATH'08 Proceedings of the American Conference on Applied Mathematics
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
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In this paper we evaluate similarity functions for histograms such as chi-square and Bhattacharyya distance for different color spaces such as RGB or L*a*b*. Our main contribution is to show the performance of these histogram-based similarity functions combined with several color spaces. The evaluation is done on image sequences of the PETS 2009 dataset, where a sequence of frames is used to compute the histograms of three different persons in the scene. One of the most popular applications where similarity functions can be used is tracking. Data association is done in multiple stages where the first stage is the computation of the similarity of objects between two consecutive frames. Our evaluation concentrates on this first stage, where we use histograms as data type to compare the objects with each other. In this paper we present a comprehensive evaluation on a dataset of segmented persons with all combinations of the used similarity functions and color spaces.