Vocabulary Tree schema based on SURF descriptor for real time object detection and recognition in video

In this paper we describe an automated process of object detection and recognition in real time video scene having a fix background. The objective is to provide a real time system which can detect and recognize any new object introduced in the video. This detection is performed based on an adaptive Gaussian Model which re-estimates the background model permanently. Each detected object is then described with a local detector and descriptor of key points SURF chosen as it is invariant, robust and distinctive. Finally the proposed process leads to a highly performed identification in a database of images based on the structure of the Vocabulary Tree. This paper presents also experimental results to evaluate the performance of the algorithms which confirm the high performance and the robustness of our approach.