A Comparison and Evaluation of Motion Indexing Techniques

Motion indexing concerns efficient ways to identify and retrieve motions similar to a query motion from a large set of motions stored in a human motion database. In this paper, we perform the first quantitative evaluation and comparison of motion indexing techniques. We extend PCA-based algorithms for motion segmentation to address the motion indexing problem and perform a survey of the most significant motion indexing techniques in the literature. We implement five different techniques for motion indexing: two principal component analysis (PCA) based methods, a feature-based method, and two dynamic time warping (DTW) based methods. The indexing accuracy is evaluated for all techniques and a quantitative comparison among them is achieved. The two PCA-based techniques have the lowest number of false negatives but, at the same time, they have a large number of false positives (close to 90%). The feature-based and DTW quaternion-based techniques perform better than the PCA-based techniques. While the DTW-3D technique has a small number of false positives, the false negatives are also very few. The Dynamic Time Warping 3D-based technique performed best among all techniques when compared by false positives and false negatives metrics.