Csaba Beleznai (1) , Martin Winter (2), Martin Hirzer (2), Horst Bischof (2), Josef Birchbauer (3)
(1) Austrian Research Centers GmbH – ARC, Vienna, Austria
(2) Institute for Computer Graphics and Vision, Graz University of Technology, Austria
(3) Siemens Corporate Technology Central Eastern Europe, Siemens AG Austria
The below video demonstrates the search mechanisms of the visual search framework (Note that individual steps depicted in the video are edited/shortened in order to provide a concise summary):
- An arbitrary pedestrian (which has been previously detected and tracked by a surveillance system) is selected manually to form the query.
- An initial search is performed using a fixed set of features and a first ranked list of hypothical matches is returned (40 images shown in two rows).
- If the searched pedestrian is not among the returned hypotheses, a limited set of samples - representing the worst matches - are automatically labelled as negatives. On-line boosting is used for feature selection and a new ranked list of possible matches is generated.
- If the search is unsuccessful, an interactive labelling is performed, marking pedestrian images which look similar to the query as positive samples (marked green in the video) and dissimilar ones as negative samples (marked red in the video).
- Typically, after few iterations the correct match is retrieved.