Nearest First Traversing Graph for Simultaneous Object Tracking and Recognition

This paper presents a new method for simultaneous object tracking and recognition using object image database. This application requires two searches: search for object appearance stored in the database and that for pose parameters (position, scale, orientation, and so on) of the tracking object in each image frame. For simplifying this problem, we propose a new method, pose parameter embedding (PPE) that transforms the original problem to an appearance search problem. The nearest neighbor (NN) appearance search in this problem has a special property that gradually changing queries are given. For this problem, graph based NN search is suitable, because the preceding search result can be used as the starting point of the next search. Delaunay graph can be used for this search, however, both the graph construction cost and the degree (number of mean edges connected to a vertex) drastically increase in high-dimensional space. Instead, we propose nearest first traversing graph (NFTG) for avoiding these problems. Based on these two techniques, we successfully realized video-rate tracking and recognition.