Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 3 of 6
Back to Result List

Human Detection and Action Recognition in Video Sequences: Human Character Recognition in TV-Style Movies

  • This master thesis describes a supervised approach to the detection and the identification of humans in TV-style video sequences. In still images and video sequences, humans appear in different poses and views, fully visible and partly occluded, with varying distances to the camera, at different places, under different illumination conditions, etc. This diversity in appearance makes the task of human detection and identification to a particularly challenging problem. A possible solution of this problem is interesting for a wide range of applications such as video surveillance and content-based image and video processing. In order to detect humans in views ranging from full to close-up view and in the presence of clutter and occlusion, they are modeled by an assembly of several upper body parts. For each body part, a detector is trained based on a Support Vector Machine and on densely sampled, SIFT-like feature points in a detection window. For a more robust human detection, localized body parts are assembled using a learned model for geometric relations based on Gaussians. For a flexible human identification, the outward appearance of humans is captured and learned using the Bag-of-Features approach and non-linear Support Vector Machines. Probabilistic votes for each body part are combined to improve classification results. The combined votes yield an identification accuracy of about 80% in our experiments on episodes of the TV series "Buffy the Vampire Slayer". The Bag-of-Features approach has been used in previous work mainly for object classification tasks. Our results show that this approach can also be applied to the identification of humans in video sequences. Despite the difficulty of the given problem, the overall results are good and encourage future work in this direction.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Availability


Document Type:Master's Thesis
Author:Alexander Kläser
Pagenumber:viii, 80
Referee:Rainer Herpers, Cordelia Schmid
Granting Institution:Fachhochschule Bonn-Rhein-Sieg, Fachbereich Informatik
Contributing Corporation:Inria Rhône-Alpes
Date of first publication:2011/06/06
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Theses:Fachbereich / Informatik
Entry in this database:2015/11/11