DocumentCode :
693219
Title :
Object tracking with Kalman filter and discrete cosine transform
Author :
Jun Wang ; Dansong Cheng ; Xing Feng ; Guohua Zhang
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
Volume :
04
fYear :
2013
fDate :
14-17 July 2013
Firstpage :
1501
Lastpage :
1505
Abstract :
Object tracking is important in the field of computer vision which is used to track targets in video from frame to frame. Kalman filter is a technique which can calculate the predicted position of the target in the next frame by using the history trajectory information. Knowing the predicted position of the target, image template matching is used to get the measured position around the predicted position. Discrete cosine transform (DCT) has a strong energy compaction property, so it can be used to extract features of the target effectively. In this paper, we use DCT to extract features and this does improve the speed of image template matching. Further more, we present a method of adaptively adjusting the threshold determining whether the target is sheltered or not¿ The experimental results show that our method outperforms the traditional Kalman filters.
Keywords :
Kalman filters; computer vision; discrete cosine transforms; feature extraction; image matching; object tracking; video signal processing; DCT; Kalman filter; computer vision; discrete cosine transform; feature extraction; history trajectory information; image template matching; object tracking; target tracking; video tracking; Abstracts; Accuracy; Computers; Discrete cosine transforms; Kalman filters; Tracking; Adaptive thresholds; Discrete Cosine Transform(DCT); Kalman filter; Objec Tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location :
Tianjin
Type :
conf
DOI :
10.1109/ICMLC.2013.6890842
Filename :
6890842
Link To Document :
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