目标检测工具箱

  • L8_112627
    了解作者
  • 15.8MB
    文件大小
  • zip
    文件格式
  • 0
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  • 0
    下载次数
  • 2022-04-28 11:36
    上传日期
该工具箱包含了多种运动目标检测算法,针对场景选取不同的算法来满足你的结果。
bgslibrary_x86_v1.9.2_with_mfc_gui_v1.4.2.zip
  • bgslibrary_x86_v1.9.2_with_mfc_gui_v1.4.2
  • dataset
  • video.avi
    1MB
  • config
  • AdaptiveSelectiveBackgroundLearning.xml
    260B
  • MixtureOfGaussianV1BGS.xml
    193B
  • DPAdaptiveMedianBGS.xml
    184B
  • FuzzyChoquetIntegral.xml
    341B
  • GMG.xml
    201B
  • AdaptiveBackgroundLearning.xml
    256B
  • LBFuzzyGaussian.xml
    220B
  • WeightedMovingVarianceBGS.xml
    185B
  • KDE.xml
    358B
  • DPWrenGABGS.xml
    213B
  • LBMixtureOfGaussians.xml
    219B
  • T2FGMM_UV.xml
    249B
  • T2FGMM_UM.xml
    249B
  • SuBSENSEBGS.xml
    386B
  • SigmaDeltaBGS.xml
    157B
  • LBSimpleGaussian.xml
    188B
  • WeightedMovingMeanBGS.xml
    221B
  • DPEigenbackgroundBGS.xml
    178B
  • FrameDifferenceBGS.xml
    153B
  • LBAdaptiveSOM.xml
    285B
  • FuzzySugenoIntegral.xml
    341B
  • T2FMRF_UM.xml
    228B
  • StaticFrameDifferenceBGS.xml
    153B
  • VuMeter.xml
    230B
  • MultiCueBGS.xml
    88B
  • DPTextureBGS.xml
    88B
  • IndependentMultimodalBGS.xml
    88B
  • LBFuzzyAdaptiveSOM.xml
    285B
  • MixtureOfGaussianV2BGS.xml
    193B
  • DPZivkovicAGMMBGS.xml
    182B
  • DPGrimsonGMMBGS.xml
    181B
  • MultiLayerBGS.xml
    1.7KB
  • DPMeanBGS.xml
    194B
  • LOBSTERBGS.xml
    364B
  • DPPratiMediodBGS.xml
    198B
  • T2FMRF_UV.xml
    228B
  • PixelBasedAdaptiveSegmenter.xml
    166B
  • outputs
  • foreground
  • input
  • background
  • opencv_ffmpeg2410.dll
    10MB
  • mfc_bgslibrary.exe
    6.2MB
  • mfc_bgslibrary.pdb
    32.2MB
内容介绍
<?xml version="1.0"?> <opencv_storage> <preloadModel>"./models/MultiLayerBGSModel.yml"</preloadModel> <saveModel>0</saveModel> <detectAfter>0</detectAfter> <disableDetectMode>1</disableDetectMode> <disableLearningInDetecMode>0</disableLearningInDetecMode> <loadDefaultParams>1</loadDefaultParams> <max_mode_num>5</max_mode_num> <weight_updating_constant>5.</weight_updating_constant> <texture_weight>5.0000000000000000e-001</texture_weight> <bg_mode_percent>6.0000002384185791e-001</bg_mode_percent> <pattern_neig_half_size>4</pattern_neig_half_size> <pattern_neig_gaus_sigma>3.</pattern_neig_gaus_sigma> <bg_prob_threshold>2.0000000298023224e-001</bg_prob_threshold> <bg_prob_updating_threshold>2.0000000298023224e-001</bg_prob_updating_threshold> <robust_LBP_constant>3</robust_LBP_constant> <min_noised_angle>1.7453293502330780e-001</min_noised_angle> <shadow_rate>6.0000002384185791e-001</shadow_rate> <highlight_rate>1.2000000476837158e+000</highlight_rate> <bilater_filter_sigma_s>3.</bilater_filter_sigma_s> <bilater_filter_sigma_r>1.0000000149011612e-001</bilater_filter_sigma_r> <frame_duration>1.0000000149011612e-001</frame_duration> <learn_mode_learn_rate_per_second>5.0000000000000000e-001</learn_mode_learn_rate_per_second> <learn_weight_learn_rate_per_second>5.0000000000000000e-001</learn_weight_learn_rate_per_second> <learn_init_mode_weight>5.0000000745058060e-002</learn_init_mode_weight> <detect_mode_learn_rate_per_second>9.9999997764825821e-003</detect_mode_learn_rate_per_second> <detect_weight_learn_rate_per_second>9.9999997764825821e-003</detect_weight_learn_rate_per_second> <detect_init_mode_weight>1.0000000474974513e-003</detect_init_mode_weight> <showOutput>0</showOutput> </opencv_storage>
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