Overview of the ImageCLEF@ICPR 2010 Robot Vision Track

• Published in: Recognizing Patterns in Signals, Speech, Images and Videos pp. 171 - 179
• Main Authors: Pronobis, Andrzej, Christensen, Henrik I., Caputo, Barbara
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Series: Lecture Notes in Computer Science
• Subjects: Information technology; Informationsteknik; Maskinteknik; Mechanical engineering; Other technology; Place recognition; robot localization; robot vision; Systems engineering; Systemteknik; TECHNOLOGY; TEKNIKVETENSKAP; Övriga teknikvetenskaper
• ISBN: 9783642177101; 3642177107
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-17711-8_18

This paper describes the robot vision track that has been proposed to the ImageCLEF@ICPR2010 participants. The track addressed the problem of visual place classification. Participants were asked to classify rooms and areas of an office environment on the basis of image sequences captured by a stereo camera mounted on a mobile robot, under varying illumination conditions. The algorithms proposed by the participants had to answer the question “where are you?” (I am in the kitchen, in the corridor, etc) when presented with a test sequence imaging rooms seen during training (from different viewpoints and under different conditions), or additional rooms that were not imaged in the training sequence. The participants were asked to solve the problem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). A total of eight groups participated to the challenge, with 25 runs submitted to the obligatory task, and 5 submitted to the optional task. The best result in the obligatory task was obtained by the Computer Vision and Geometry Laboratory, ETHZ, Switzerland, with an overall score of 3824.0. The best result in the optional task was obtained by the Intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain, with an overall score of 3881.0.