[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000002629":3},{"id":4,"title":5,"authors":6,"files":8,"advisors":35,"authors_detail":36,"advisors_detail":40,"publisher":41,"publish_place":42,"language":43,"type":44,"type_slug":45,"year":46,"department":47,"study_program":48,"keywords":49,"keywords_detail":56,"abstract":69,"abstract_html":70,"url":71},"repo-dspace000002629","Gait Recognition from Surveillance Footage Using Deep Learning",[7],"Khafillah Akbar Syahputra",[9,13,16,19,23,26,29,32],{"chapter":10,"requires_login":11,"label":12,"available":11},"abstrak",false,"Abstrak",{"chapter":14,"requires_login":11,"label":15,"available":11},"bab1","Bab I",{"chapter":17,"requires_login":11,"label":18,"available":11},"bab2","Bab II",{"chapter":20,"requires_login":21,"label":22,"available":11},"bab3",true,"Bab III",{"chapter":24,"requires_login":21,"label":25,"available":11},"bab4","Bab IV",{"chapter":27,"requires_login":21,"label":28,"available":11},"bab5","Bab V",{"chapter":30,"requires_login":21,"label":31,"available":11},"full","Dokumen Lengkap",{"chapter":33,"requires_login":11,"label":34,"available":21},"open","Akses Terbuka",[],[37],{"name":7,"nim":38,"slug":39},"2141720152","khafillah-akbar-syahputra",[],"Politeknik Negeri Malang","Malang","Inggris","Skripsi (D4)","skripsi-d4",2025,"D-IV TEKNIK INFORMATIKA",null,[50,51,52,53,54,55],"Gait Recognition","Deep Learning","Long Short Term Memory (LSTM)","OU-ISIR MVLP","CASIA-B","Surveillance",[57,59,61,63,65,67],{"name":50,"slug":58},"gait-recognition",{"name":51,"slug":60},"deep-learning",{"name":52,"slug":62},"long-short-term-memory-lstm",{"name":53,"slug":64},"ou-isir-mvlp",{"name":54,"slug":66},"casia-b",{"name":55,"slug":68},"surveillance","Gait recognition is a biometric method that identifies individuals based on their unique walking patterns. This study aims to develop a gait recognition system from surveillance scenarios using public gait datasets. using a deep learning approach based on Long Short-Term Memory (LSTM) networks. Two datasets are utilized: OU-ISIR MVLP and CASIA-B, each offering different characteristics in terms of subject diversity, viewpoint variations, and data collection conditions. The system includes stages of silhouette image preprocessing, feature extraction, model training, and testing through a web-based interface. Training results indicate that the LSTM model can recognize gait patterns with varying accuracy depending on the dataset and viewing angle. In tests using the CASIA-B dataset, the model achieved correct predictions with a maximum confidence score of 0.7412 at certain angles, while the OU-ISIR MVLP dataset produced less consistent predictions with lower average confidence scores. The system was implemented using a web-based architecture built with Next.js and Flask. This research provides an early overview of the potential and limitations of applying LSTM models in silhouette-based gait recognition.","\u003Cp>Gait recognition is a biometric method that identifies individuals based on their unique walking patterns. This study aims to develop a gait recognition system from surveillance scenarios using public gait datasets. using a deep learning approach based on Long Short-Term Memory (LSTM) networks. Two datasets are utilized: OU-ISIR MVLP and CASIA-B, each offering different characteristics in terms of subject diversity, viewpoint variations, and data collection conditions. The system includes stages of silhouette image preprocessing, feature extraction, model training, and testing through a web-based interface. Training results indicate that the LSTM model can recognize gait patterns with varying accuracy depending on the dataset and viewing angle. In tests using the CASIA-B dataset, the model achieved correct predictions with a maximum confidence score of 0.7412 at certain angles, while the OU-ISIR MVLP dataset produced less consistent predictions with lower average confidence scores. The system was implemented using a web-based architecture built with Next.js and Flask. This research provides an early overview of the potential and limitations of applying LSTM models in silhouette-based gait recognition.\u003C\u002Fp>","https:\u002F\u002Frepository.polinema.ac.id\u002Fcollection\u002Fdetail?id=dspace000002629&year=2025&title=gait-recognition-from-surveillance-footage-using-deep-learning"]