Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
Human motion analysis: a review
Computer Vision and Image Understanding
Latent semantic indexing: a probabilistic analysis
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Negotiating the Semantic Gap: From Feature Maps to Semantic Landscapes
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Describing motion for recognition
ISCV '95 Proceedings of the International Symposium on Computer Vision
Automatic gait recognition by symmetry analysis
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Stride and Cadence as a Biometric in Automatic Person Identification and Verification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
Simplest Representation Yet for Gait Recognition: Averaged Silhouette
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Outdoor recognition at a distance by fusing gait and face
Image and Vision Computing
A Framework for Ontology Enriched Semantic Annotation of CCTV Video
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Human recognition on combining kinematic and stationary features
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
What image information is important in silhouette-based gait recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A linear-algebraic technique with an application in semantic image retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gait Components and Their Application to Gender Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Human attributes from 3D pose tracking
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Human attributes from 3D pose tracking
Computer Vision and Image Understanding
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In order to analyse surveillance video, we need to efficiently explore large datasets containing videos of walking humans. Effective analysis of such data relies on retrieval of video data which has been enriched using semantic annotations. A manual annotation process is time-consuming and prone to error due to subject bias however, at surveillance-image resolution, the human walk (their gait) can be analysed automatically. We explore the content-based retrieval of videos containing walking subjects, using semantic queries. We evaluate current research in gait biometrics, unique in its effectiveness at recognising people at a distance. We introduce a set of semantic traits discernible by humans at a distance, outlining their psychological validity. Working under the premise that similarity of the chosen gait signature implies similarity of certain semantic traits we perform a set of semantic retrieval experiments using popular Latent Semantic Analysis techniques. We perform experiments on a dataset of 2000 videos of people walking in laboratory conditions and achieve promising retrieval results for features such as Sex (mAP 驴=驴 14% above random), Age (mAP 驴=驴 10% above random) and Ethnicity (mAP 驴=驴 9% above random).