Streamlining collection of training samples for object detection and classification in video

This paper is concerned with object recognition and detection in computer vision. Many promising approaches in the field exploit the knowledge contained in a collection of manually annotated training samples. In the resulting paradigm, the recognition algorithm is automatically constructed by some machine learning technique. It has been shown that the quantity and quality of positive and negative training samples is critical for good performance of such approaches. However, collecting the samples requires tedious manual effort which is expensive in time and prone to error. In this paper we present design and implementation of a software system which addresses these problems. The system supports an iterative approach whereby the current state-of-the-art detection and recognition algorithms are used to streamline the collection of additional training samples. The presented experiments have been performed in the frame of a research project aiming at automatic detection and recognition of traffic signs in video.