A Novel Efficient Algorithm for Locating and Tracking Object Parts in Low Resolution Videos

Abstract

In this paper, a novel efficient algorithm is presented for locating and tracking object parts in low resolution videos using Lowe's SIFT keypoints with a nearest neighbor object detection approach. Our interest lies in using this information as one step in the process of automatically programming service, household, or personal robots to perform the skills that are being taught in easily obtainable instructional videos. In the reported experiments, the system looked for 14 parts of inanimate and animate objects in 40 natural outdoor scenes. The scenes were frames from a low-resolution instructional video on cleaning golf clubs containing 2,405 frames of 180 by 240 pixels. The system was trained using 39 frames that were half-way between the test frames. Despite the low resolution quality of the instructional video and occluded training samples, the system achieved a recall of 49% with a precision of 71% and an F1 of 0.58, which is better than that achieved by less demanding applications. In order to verify that the reported results were not dependent on the specific video, the proposed technique was applied to another video and the results are reported.