To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance\nlearning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is\napplied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and\nclassification scores of all the blocks for detecting the object. In particular, compared with thewhole object based MIL algorithm, the\nP-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object,\nthe learning rates for updatingweak classifiers� parameters are adaptively tuned.The classifier updating strategy avoids overupdating\nand underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several\nclassical videos.Theexperiment results illustrate that the proposed method performs well especially in case of illumination changes\nor pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.
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