Detection and tracking of humans and faces

Authors:
Stefan Karlsson;Murtaza Taj;Andrea Cavallaro
Affiliations:
Multimedia and Vision Group, Queen Mary University of London, London, UK;Multimedia and Vision Group, Queen Mary University of London, London, UK;Multimedia and Vision Group, Queen Mary University of London, London, UK
Venue:
Journal on Image and Video Processing - Regular
Year:
2008

Citing 11
Cited 2

Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Faces

FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Detecting Pedestrians Using Patterns of Motion and Appearance

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Unified Stochastic Model for Detecting and Tracking Faces

CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

International Journal of Computer Vision
Interaction between high-level and low-level image analysis for semantic video object extraction

EURASIP Journal on Applied Signal Processing
Integration of Bayes detection with target tracking

IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Eigenspace-based face recognition: a comparative study of different approaches

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image change detection algorithms: a systematic survey

IEEE Transactions on Image Processing

Content and task-based view selection from multiple video streams

Multimedia Tools and Applications
Face tracking using adaptive appearance models and convolutional neural network

HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a video analysis framework that integrates prior knowledge in object tracking to automatically detect humans and faces, and can be used to generate abstract representations of video (key-objects and object trajectories). The analysis framework is based on the fusion of external knowledge, incorporated in a person and in a face classifier, and low-level features, clustered using temporal and spatial segmentation. Low-level features, namely, color and motion, are used as a reliability measure for the classification. The results of the classification are then integrated into a multitarget tracker based on a particle filter that uses color histograms and a zero-order motion model. The tracker uses efficient initialization and termination rules and updates the object model over time. We evaluate the proposed framework on standard datasets in terms of precision and accuracy of the detection and tracking results, and demonstrate the benefits of the integration of prior knowledge in the tracking process.