Abstract-Being able to identify and localize objects is an important requirement for various humanoid robot applications. In this paper we present a method which uses PCA-SIFT in combination with a clustered voting scheme to achieve detection and localization of multiple objects in real-time video data. Our approach provides robustness against constraints that are common for humanoid vision systems such as perspective changes, partial occlusion, and motion blurring. We analyze and evaluate the performance of our method in two concrete humanoid testscenarios. I. INTRODUCTION SIFT (Scale Invariant Feature Transform) has been shown to be an effective descriptor for traditional object recognition applications in static images. In this paper, we propose and evaluate a method that uses PCA-SIFT [1] in combination with a clustered voting scheme to achieve detection and localization of multiple objects in video footage as it is typically collected by a humanoid robot's vision system. A common constraint of humanoid vision is the occurrence of frequent perspective changes and motion blurring which are often caused by the robot's walking motions. Our approach attempts to minimize the effects of these problems while still being applicable as a real-time algorithm. A flowchart of our approach is depicted in figure 1(a) . First, a training video is recorded by observing the object from various perspectives. PCA-SIFT keypoints are then generated for each video frame. It should be emphasized that by training on an entire video sequence, we are able to capture impressions of the object from a continuous spectrum of poses, and under various configurations of lighting and video noise. This allows us to gain a more complete representation of the object in PCA-SIFT space compared to training from very few still images. The keypoint generation stage is followed by a manual annotation step which allows the user to approximate the boundary and position of the relevant object in the video. All keypoints that lie outside of the hand-annotated boundary are rejected. The annotation step is furthermore used to determine each keypoint's relative location toward the annotated object's center. All retained features build the initial training dataset. The dataset is then post-processed by a clustering algorithm to compress its overall size. During the recognition stage, we again generate PCA-SIFT keypoints for each incoming video frame. A nearest neighbor search is performed on the training dataset for each PCA-SIFT keypoint while enforcing a maximum distance threshold.
Stefan and Manuela proposed and evaluated a method that used PCA-SIFT in combination with a clustered voting scheme with reasonable performance REF .
Detection and Localization of Multiple Objects
2006 6th IEEE-RAS International Conference on Humanoid Robots
2006 6th IEEE-RAS International Conference on Humanoid Robots
Computer Science