Description: We present a parallel implementation of a histogram-based particle filter for object tracking on smart
cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights
and parallel construction of the feature histograms since these are the major bottlenecks in standard
implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-
based feature sets—we show in detail how the parallel particle filter can employ simple color histograms
as well as more complex histograms of oriented gradients (HOG). The algorithm was successfully
implemented on an SIMD processor and performs robust object tracking at up to 30 frames per second—a
performance difficult to achieve even on a modern desktop computer.
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OLT\.ttf\VeraMono.ttf
...\Changes
...\dump4mc
...\Gcc\dump4mc
...\...\getFastHOG
...\...\lightclassify_FastHOG
...\...\svm_classify
...\...\svm_learn
...\getFastHOG
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...\...\FullTest_Negative.Margin4\histogram.txt
...\...\.........................\list.txt
...\...\.........Positive.Margin4\histogram.txt
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...\...\WindowTest_Negative\histogram.txt
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...\...\libImlib2.so.1.2.0
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...\lightclassify_FastHOG
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...\svm_classify
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...\HOG\FullTest_Negative.Margin4
...\...\FullTest_Positive.Margin4
...\...\hard
...\...\WindowTest_Negative
...\...\WindowTest_Positive
...\.ttf
...\Gcc
...\HOG
...\Intel
...\lib
...\matlab
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