Bayesian Multiple Person Tracking Using Probability Hypothesis Density Smoothing
نویسندگان
چکیده
We presents a PHD filtering approach to estimate the state of an unknown number of persons in a video sequence. Persons are represented by moving blobs, which are tracked across different frames using a first-order moment approximation to the posterior density. The PHD filter is a good alternative to standard multi-target tracking algorithms, since overrides making explicit associations INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 4, NO. 2, JUNE 2011
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تاریخ انتشار 2011