Abstract：A particle filter tracking algorithm based on multi-feature clustering is proposed.To address the issues such as the diversity of target features,the difference between methods of feature distribution description,and the arbitrariness of feature spatial structure,the multi-features representation of target model is unified into a clustering computing framework. The mean shift based feature space analysis approach is employed to adaptively calculate the clusters in any arbitrarily structured feature space.Based on the clusters,a target probability density estimation method,which is efficient and accurate, is proposed to represent the target model.The distance between the reference target and the candidate is calculated by the similarity measure of kernel density estimation,and is taken as important information for observation in particle filter system. To efficiently enhance the utilization rate of particles,an improved particle propagation model is presented.The object tracking experiments are performed on many real image sequences by using the LUV color features and the LBP(local binary pattern) texture features.Experiment results show that the proposed algorithm can obtain high tracking accuracy and strong robustness,meet real-time demand,and provide better tracking performance comparing with other typical algorithms.
 Comaniciu D,Ramesh V,Meer P.Kernel-based object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5):564-577.
 Bradski G R.Real time face and object tracking as a component of a perceptual user interface[C]//4th IEEE Workshop on Applications of Computer Vision.Piscataway,NJ,USA:IEEE,1998:214-219.
 Li P H.A clustering-based color model and integral images for fast object tracking[J].Signal Processing:Image Communication,2006,21(8):676-687.
 Gotlieb C C,Kreyszig H E.Texture descriptors based on cooccurrence matrices[J].Computer Vision,Graphics,and Image Processing,1990,51(1):70-86.
 Van de Wouwer G,Scheunders P,Van Dyck D.Statistical texture characterization from discrete wavelet representations[J].IEEE Transactions on Image Processing,1999,8(4):592-598.
 Leung T,Malik J.Representing and recognizing the visual appearance of materials using three-dimensional textons[J],International Journal of Computer Vision,2001,43(1):29-44.
 Ojala T,Pietikainen M,Maenpa T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
 Ning J F,Zhang L.,Zhang D,et al.Robust object tracking using joint color-texture histogram[J].International Journal of Pattern Recognition and Artificial Intelligence,2009,23(7):1245-1263.
 Birchfield S.Elliptical head tracking using intensity gradients and color histograms[C]//Conference on Computer Vision and Pattern Recognition.Piscataway,NJ,USA:IEEE,1998:232-237.
 Seemann E,Leibe B,Mikolajczyk K,et al.An evaluation of local shape-based features for pedestrian detection[C]//British Machine Vision Conference.2005.
 Wang J Q,Yagi Y.Integrating color and shape-texture features for adaptive real-time object tracking[J].IEEE Transactions on Image Processing,2008,17(2):235-240.
 Comaniciu D,Meer P.Mean shift:A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine intelligence,2002,24(5):603-614.
 Christoudias C M,Georgescu B,Meer P.Synergism in low level vision[C]//16th International Conference on Pattem Recognition.Piscataway,NJ,USA:IEEE,2002:150-155.
 Yang C J,Duraiswami R,Davis L.Efficient mean-shift tracking via a new similarity measure[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Piscataway,NJ,USA:IEEE,2005:176-183.
 Arulampalam M S,Maskell S,Gordon N,et al.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174-188.
 Stan Birchfield.Test image sequences for face tracking[EB/OL].[2010-12-27].http://www.ces.clemson.edu/～stb/research/headtracker/.
 Adam A,Rivlin E,Shimshoni I.Robust fragments-based tracking using the integral histogram[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Piscataway,NJ,USA:IEEE,2006:798-805.