Key-frame Selection based Online Object Learning and Detection

Abstract

In this paper, we propose a keyframe-based online object learning and detection method. To manage appearance changes of target objects, the proposed method incrementally updates an object database using detection results. One of the major problems in updating the appearance model is that the object model can gradually be degraded by accumulated errors and biased to specific views. To solve this problem, our object model is updated according to the selected keyframes, which not only help memorize important views of target objects, but also prevent the holistic appearance model from overfitting. The database is represented as a graph of the registered images, and the importance of the database images is measured by analyzing the constructed graph. Then, the redundant or less important images are discarded from the database. As a result, the database is efficiently maintained while new views of the objects are gradually added. The experimental results demonstrate that the proposed algorithm efficiently maintains the object database and improves the detection performance compared to previous incremental object learning and detection algorithms.