Similarity-Based Processing of Motion Capture Data

Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. The recorded data can be imprecise, voluminous, and the same movement action can be performed by various subjects in a number of alternatives that can vary in speed, timing or a position in space. This requires employing completely different data-processing paradigms compared to the traditional domains such as attributes, text or images. The objective of this tutorial is to explain fundamental principles and technologies designed for similarity comparison, searching, subsequence matching, classification and action detection in the motion capture data. Specifically, we emphasize the importance of similarity needed to express the degree of accordance between pairs of motion sequences and also discuss the machine-learning approaches able to automatically acquire content-descriptive movement features. We explain how the concept of similarity together with the learned features can be employed for searching similar occurrences of interested actions within a long motion sequence. Assuming a user-provided categorization of example motions, we discuss techniques able to recognize types of specific movement actions and detect such kinds of actions within continuous motion sequences. Selected operations will be demonstrated by on-line web applications

Recognition of Human Periodic Movements From Unstructured Information Using A Motion-based Frequency Domain Approach

Feature-based motion cues play an important role in biological visual perception. We present a motion-based frequency-domain scheme for human periodic motion recognition. As a baseline study of feature based recognition we use unstructured feature-point kinematic data obtained directly from a marker-based optical motion capture (MoCap) system, rather than accommodate bootstrapping from the low-level image processing of feature detection. Motion power spectral analysis is applied to a set of unidentified trajectories of feature points representing whole body kinematics. Feature power vectors are extracted from motion power spectra and mapped to a low dimensionality of feature space as motion templates that offer frequency domain signatures to characterise different periodic motions. Recognition of a new instance of periodic motion against pre-stored motion templates is carried out by seeking best motion power spectral similarity. We test this method through nine examples of human periodic motion using MoCap data. The recognition results demonstrate that feature-based spectral analysis allows classification of periodic motions from low-level, un-structured interpretation without recovering underlying kinematics. Contrasting with common structure-based spatio-temporal approaches, this motion-based frequency-domain method avoids a time-consuming recovery of underlying kinematic structures in visual analysis and largely reduces the parameter domain in the presence of human motion irregularities

On Rendering Synthetic Images for Training an Object Detector

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available

Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures

Combining inertial and visual sensing for human action recognition in tennis

In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration

DC-image for real time compressed video matching

This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity