Robust Person Detection for Surveillance Using Online Learning

Abstract

Recently, there has been considerable amount of research in methods for person detection. This talk will focus on methods for person detection in a surveillance setting (known environment). We will demonstrate that in this setting one can build robust and highly reliable person detectors by using on-line learning methods. In particular, I will first discuss ldquoconservative learningrdquo which is able to learn a person detector without any hand labelling effort. As a second example I will discuss a recently developed grid based person detector. The basic idea is to considerably simplify the detection problem by considering individual image locations separately. Therefore, we can use simple adaptive classifiers which are trained on-line. Due to the reduced complexity we can use a simple update strategy that requires only a few positive samples and is stable by design. This is an essential property for real world applications which require operation for 24 hours a day, 7 days a week. During the talk we will illustrate our results on video sequences and standard benchmark databases.