IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Pedestrian Models for Silhouette Refinement
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Shape and motion driven particle filtering for human body tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Gait analysis for human identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
3D facial pose tracking in uncalibrated videos
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Identification of humans using gait
IEEE Transactions on Image Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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Images constitute data that live in a very high dimensional space, typically of the order of hundred thousand dimensions. Drawing inferences from correlated data of such high dimensions often becomes intractable. Therefore traditionally several of these problems like face recognition, object recognition, scene understanding etc. have been approached using techniques in pattern recognition. Such methods in conjunction with methods for dimensionality reduction have been highly popular and successful in tackling several image processing tasks. Of late, the advent of cheap, high quality video cameras has generated new interests in extending still image-based recognition methodologies to video sequences. The added temporal dimension in these videos makes problems like face and gait-based human recognition, event detection, activity recognition addressable. Our research has focussed on solving several of these problems through a pattern recognition approach. Of course, in video streams patterns refer to both patterns in the spatial structure of image intensities around interest points and temporal patterns that arise either due to camera motion or object motion. In this paper, we discuss the applications of pattern recognition in video to problems like face and gait-based human recognition, behavior classification, activity recognition and activity based person identification.