TY - GEN
T1 - Probabilistic index histogram for robust object tracking
AU - Li, Wei
AU - Zhang, Xiaoqin
AU - Xie, Nianhua
AU - Hu, Weiming
AU - Luo, Wenhan
AU - Ling, Haibin
PY - 2011
Y1 - 2011
N2 - Color histograms are widely used for visual tracking due to their robustness against object deformations. However, traditional histogram representation often suffers from problems of partial occlusion, background cluttering and other appearance corruptions. In this paper, we propose a probabilistic index histogram to improve the discriminative power of the histogram representation. With this modeling, an input frame is translated into an index map whose entries indicate indexes to a separate bin. Based on the index map, we introduce spatial information and the bin-ratio dissimilarity in histogram comparison. The proposed probabilistic indexing technique, together with the two robust measurements, greatly increases the discriminative power of the histogram representation. Both qualitative and quantitative evaluations show the robustness of the proposed approach against partial occlusion, noisy and clutter background.
AB - Color histograms are widely used for visual tracking due to their robustness against object deformations. However, traditional histogram representation often suffers from problems of partial occlusion, background cluttering and other appearance corruptions. In this paper, we propose a probabilistic index histogram to improve the discriminative power of the histogram representation. With this modeling, an input frame is translated into an index map whose entries indicate indexes to a separate bin. Based on the index map, we introduce spatial information and the bin-ratio dissimilarity in histogram comparison. The proposed probabilistic indexing technique, together with the two robust measurements, greatly increases the discriminative power of the histogram representation. Both qualitative and quantitative evaluations show the robustness of the proposed approach against partial occlusion, noisy and clutter background.
UR - https://openalex.org/W1903436970
UR - https://www.scopus.com/pages/publications/80053110078
U2 - 10.1007/978-3-642-22822-3_19
DO - 10.1007/978-3-642-22822-3_19
M3 - Conference Paper published in a book
SN - 9783642228216
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 194
BT - Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
T2 - International Workshops on Computer Vision, ACCV 2010
Y2 - 8 November 2010 through 9 November 2010
ER -