Active Learning from Video Streams in a Multi-Camera Scenario

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Samaneh Khoshrou (Author) Other; Jaime S Cardoso (Author) FEUP; Luis F Teixeira (Author) FEUP

Conference proceedings International

Title: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)

Pages: 1248-1253

22nd International Conference on Pattern Recognition (ICPR)

Stockholm, SWEDEN, AUG 24-28, 2014

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-00A-4DD

DOI: 10.1109/icpr.2014.224

Abstract (EN): While video surveillance systems are spreading everywhere, extracting meaningful information from what they are recording is still prohibitively expensive. There is a major effort under way in order to make this process economical by including an intelligent software that eases the burden of the system. In this paper, we introduce an incremental learning framework to classify parallel data streams generated in a multi-camera surveillance scenario. The framework exploits active learning strategies in order to interact wisely with operators to address various problems that exist in such non-stationary environments, such as concept drift and concept evolution. If we look at the problem as mining parallel streams, the framework can address learning from uneven parallel streams applying a class-based ensemble, a problem that has not been addressed before. Favourable results indicate the success of the framework.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6