Context-based object detection in still images

N.H. Bergboer, E.O. Postma, H.J. van den Herik

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We present a novel dual-stage object-detection method. In the first stage, an object detector based on appropriate visual features is used to find object candidates. In the second stage, the object candidates are assigned a confidence value based on local-contextual information. Our context-based method is called COBA, for COntext BAsed object detection. At a given detection rate COBA is able to lower the false-detection rate. Experiments in which frontal human faces are to be detected show that the number of false positives is lowered by a factor 8.7 at a detection rate of 80% when compared to the current high-performance object detectors. Moreover, COBA is capable of flexibly using other new object-detection algorithms as 'plug-ins' in the second stage. Hence, object detection can be straightforwardly improved by our method a soon as new insights emerge and are available in algorithmic form.
Original languageEnglish
Pages (from-to)987-1000
JournalImage and Vision Computing
Volume24
DOIs
Publication statusPublished - 1 Jan 2006

Cite this

Bergboer, N. H., Postma, E. O., & van den Herik, H. J. (2006). Context-based object detection in still images. Image and Vision Computing, 24, 987-1000. https://doi.org/10.1016/j.imavis.2006.02.024