NDLI: Object Detection and Tracking Using Spatiotemporal Wavelet Domain Markov Random Field in Video

Content Provider	IEEE Xplore Digital Library
Author	Xuchao Li Dongsheng Liu
Copyright Year	2009
Abstract	In this paper, an object detection and tracking algorithm is proposed. At the object detection stage, the spatial features of object are extracted by wavelet transform, according to frame difference, the moving target is determined. In order to effectively utilize the temporal motion information, Markov random field prior probability model and the observation field model are established, taking advantage of Bayesian criterion, the location of object in successive frame is estimated, it contributes to the accurate space object detection. At the object tracking stage, using color and texture histogram of object, the Bhattacharyya coefficients are constructed, according to the color and texture Bhattacharyya distance, the joint likelihood tracker is established. In order to adapt the scene changes and enhance the tracker robustness, according to color and texture histogram, we construct Kalman filter with time-varying parameters. In the light of Kalman filter residuals statistical distribution, the candidate object model is updated by using hypothesis test. Experiments on international standard video sequences illustrate the effectiveness of the proposed schemes.
Starting Page	165
Ending Page	170
File Size	539555
Page Count	6
File Format	PDF
ISBN	9781424454112
e-ISBN	9781424454129
DOI	10.1109/CIS.2009.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Wavelet domain Target tracking Motion estimation wavelet transform Markov random field Data mining Markov random fields histogram Histograms Bayesian methods Object detection object detection and tracking Spatiotemporal phenomena Kalman filters
Content Type	Text
Resource Type	Article

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