DocumentCode :
2863825
Title :
Efficient Color-Ingredient Particle Filter for Video Object Tracking
Author :
Chen, Jian-Hui ; Tsai, Wen-kai ; Sheu, Ming-hwa ; Lin, Kai-Min ; Liao, Ho-En
Author_Institution :
Dept. & Inst. of Electron. Eng., Nat. Yunlin Univ. of Sci. & Technol., Douliou, Taiwan
fYear :
2011
fDate :
16-18 Dec. 2011
Firstpage :
49
Lastpage :
52
Abstract :
This paper proposes a new object model and a similarity measure method for particle filter. Based on cluster color histogram concept and similarity measure method, we analyze color ingredient and measure similarity using Euclidean distance, such that our approach can decrease memory consumption and increase processing speed effectively. Furthermore, in order to increase processing speed, we select the candidate particles based on the previous object segmentation. This can reduce the particle amount and speed up tracking operation. Comparing with the existing approaches, the experiments demonstrate that our method has batter performance even when moving objects go across complex scene. The proposed method can run comfortably in real time with 58 frames per second, and 4428 bytes memory consumption in average.
Keywords :
image colour analysis; image segmentation; object tracking; particle filtering (numerical methods); video signal processing; Euclidean distance; cluster color histogram; color ingredient; color-ingredient particle filter; memory consumption; object model; object segmentation; processing speed; similarity measure; video object tracking; Accuracy; Atmospheric measurements; Color; Histograms; Image color analysis; Particle filters; Particle measurements; Particle filter; similarity measure.;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovations in Bio-inspired Computing and Applications (IBICA), 2011 Second International Conference on
Conference_Location :
Shenzhan
Print_ISBN :
978-1-4577-1219-7
Type :
conf
DOI :
10.1109/IBICA.2011.17
Filename :
6118804
Link To Document :
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