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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Pattern Recognition and Tracking Using Correlation Filters

Pattern Recognition and Tracking Using Correlation Filters

Bhuiyan, Sharif M. a.

Paperback

Technology & Engineering

ISBN10: 3639042506
ISBN13: 9783639042504
Publisher: Blues Kids Of Amer
Published: Jun 27 2008
Pages: 168
Weight: 0.51
Height: 0.36 Width: 6.00 Depth: 9.00
Language: English
Four correlation filters, namely, maximum average correlation height (MACH), extended MACH (EMACH), distance classifier correlation filter (DCCF) and polynomial DCCF (PDCCF) are employed to recognize and track single and multiple identical and/or dissimilar targets. The MACH and EMACH filters are used for detection while DCCF and PDCCF filters are utilized for classification. Three different two-step algorithms are developed, based on the combination of the filters, called MACH-DCCF, MACH-PDCCF and EMACH-PDCCF algorithms. In the first step, the input scene is correlated with all the detection filters (one for each desired target class) and the resulting correlation outputs are com-bined. Then a predefined number of regions of interest (ROI) are selected from the input scene based on the higher correlation peak values. In the second step, a classification filter is applied to these ROIs to identify target types and reject clutters and backgrounds. Moving target tracking is accomplished by applying this technique independently to all incoming image frames. Simulation results are given for the proposed algorithms using forward looking infrared (FLIR) imagery.

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