Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Group-specific face verification using soft biometrics
Journal of Visual Languages and Computing
Video sequence querying using clustering of objects' appearance models
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
People reidentification in a distributed camera network
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
Pedestrian recognition with a learned metric
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Real-time stereo on GPGPU using progressive multi-resolution adaptive windows
Image and Vision Computing
Boosted human re-identification using Riemannian manifolds
Image and Vision Computing
Pattern Recognition Letters
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Learning to match appearances by correlations in a covariance metric space
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Human reidentification with transferred metric learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Object tracking across non-overlapping cameras using adaptive models
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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We propose a Visual Search Engine (ViSE) as a semi-automatic component in a surveillance system using networked cameras. The ViSE aims to assist the monitoring operation of huge amounts of captured video streams, which tracks and finds people in the video based on their primitive features with the interaction of a human operator. We address the issues of object detection and tracking, shadow suppression and color-based recognition for the proposed system. The experimental results on a set of video data with ten subjects showed that ViSE retrieves correct candidates with 83% recall at 83% precision.