Publicação
A machine learning approach to detect violent behaviour from video
| Resumo: | The automatic classification of violent actions performed by two or more persons is an important task for both societal and scientific purposes. In this paper, we propose a machine learning approach, based a Support Vector Machine (SVM), to detect if a human action, captured on a video, is or not violent. Using a pose estimation algorithm, we focus mostly on feature engineering, to generate the SVM inputs. In particular, we hand-engineered a set of input features based on keypoints (angles, velocity and contact detection) and used them, under distinct combinations, to study their effect on violent behavior recognition from video. Overall, an excellent classification was achieved by the best performing SVM model, which used keypoints, angles and contact features computed over a 60 frame image input range. |
|---|---|
| Autores principais: | Nova, David |
| Outros Autores: | Ferreira, André Leite; Cortez, Paulo |
| Assunto: | Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866876243668893696 |
|---|---|
| author | Nova, David |
| author2 | Ferreira, André Leite Cortez, Paulo |
| author2_role | author author |
| author_facet | Nova, David Ferreira, André Leite Cortez, Paulo |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Nova, David\"},{\"Person.name\":\"Ferreira, André Leite\"},{\"Person.name\":\"Cortez, Paulo\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Nova, David Ferreira, André Leite Cortez, Paulo |
| datacite.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2019-12-20T14:47:15Z |
| datacite.date.embargoed.fl_str_mv | 2019-12-20T14:47:15Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| datacite.titles.title.fl_str_mv | A machine learning approach to detect violent behaviour from video |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Nova, David Ferreira, André Leite Cortez, Paulo |
| dc.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2019-12-20T14:47:15Z |
| dc.date.embargoed.fl_str_mv | 2019-12-20T14:47:15Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/62744 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Verlag |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| dc.title.fl_str_mv | A machine learning approach to detect violent behaviour from video |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | The automatic classification of violent actions performed by two or more persons is an important task for both societal and scientific purposes. In this paper, we propose a machine learning approach, based a Support Vector Machine (SVM), to detect if a human action, captured on a video, is or not violent. Using a pose estimation algorithm, we focus mostly on feature engineering, to generate the SVM inputs. In particular, we hand-engineered a set of input features based on keypoints (angles, velocity and contact detection) and used them, under distinct combinations, to study their effect on violent behavior recognition from video. Overall, an excellent classification was achieved by the best performing SVM model, which used keypoints, angles and contact features computed over a 60 frame image input range. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/cc86e7fc-2bea-4202-b13a-8ec1c207d054/download |
| id | rum_2e6ccb062cc33ec7adc81636be5a2fef |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/62744 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/62744 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Nova, David Ferreira, André Leite Cortez, Paulo |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Springer Verlag |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engSpringer VerlagporThe automatic classification of violent actions performed by two or more persons is an important task for both societal and scientific purposes. In this paper, we propose a machine learning approach, based a Support Vector Machine (SVM), to detect if a human action, captured on a video, is or not violent. Using a pose estimation algorithm, we focus mostly on feature engineering, to generate the SVM inputs. In particular, we hand-engineered a set of input features based on keypoints (angles, velocity and contact detection) and used them, under distinct combinations, to study their effect on violent behavior recognition from video. Overall, an excellent classification was achieved by the best performing SVM model, which used keypoints, angles and contact features computed over a 60 frame image input range.application/pdfporA machine learning approach to detect violent behaviour from videoNova, DavidFerreira, André LeiteCortez, PauloHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISBNIsPartOf9783030164461ISSNIsPartOf1867-8211DOIIsPartOf10.1007/978-3-030-16447-8_92019-12-20T14:47:15Z20192019-12-20T14:34:27Z2019-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/62744http://purl.org/coar/access_right/c_abf2open accessAction recognitionMachine learningPose estimationSupport Vector MachineVideo analysis2106683 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/cc86e7fc-2bea-4202-b13a-8ec1c207d054/download |
| spellingShingle | A machine learning approach to detect violent behaviour from video Nova, David Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| status | SINGLETON |
| subject.fl_str_mv | Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| title | A machine learning approach to detect violent behaviour from video |
| title_full | A machine learning approach to detect violent behaviour from video |
| title_fullStr | A machine learning approach to detect violent behaviour from video |
| title_full_unstemmed | A machine learning approach to detect violent behaviour from video |
| title_short | A machine learning approach to detect violent behaviour from video |
| title_sort | A machine learning approach to detect violent behaviour from video |
| topic | Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| topic_facet | Action recognition Machine learning Pose estimation Support Vector Machine Video analysis |
| url | https://hdl.handle.net/1822/62744 |
| visible | 1 |