Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characte...Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.展开更多
Human Gait recognition is emerging as a supportive biometric technique in recent years that identifies the people through the way they walk. The gait recognition in model free approaches faces the challenges like spee...Human Gait recognition is emerging as a supportive biometric technique in recent years that identifies the people through the way they walk. The gait recognition in model free approaches faces the challenges like speed variation, cloth variation, illumination changes and view angle variations which result in the reduced recognition rate. The proposed algorithm selected the exhaustive angles from head to toe of a person, and also height and width of the same subject. The experiments were conducted using silhouettes with view angle variation, and cloth variation. The recognition rate is improved to the extent of 91% using Support vector machine classifier. The proposed method is evaluated using CASIA Gait Dataset B (The institute of Automation, ChineseAcademy of Sciences), China. Experimental results demonstrate that the proposed technique shows promising results using state of the art classifiers.展开更多
文摘Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.
文摘Human Gait recognition is emerging as a supportive biometric technique in recent years that identifies the people through the way they walk. The gait recognition in model free approaches faces the challenges like speed variation, cloth variation, illumination changes and view angle variations which result in the reduced recognition rate. The proposed algorithm selected the exhaustive angles from head to toe of a person, and also height and width of the same subject. The experiments were conducted using silhouettes with view angle variation, and cloth variation. The recognition rate is improved to the extent of 91% using Support vector machine classifier. The proposed method is evaluated using CASIA Gait Dataset B (The institute of Automation, ChineseAcademy of Sciences), China. Experimental results demonstrate that the proposed technique shows promising results using state of the art classifiers.
