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

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model constructionComment: 31 pages, 26 figure

CHAracterization of Relevant Attributes using Cyber Trajectory Similarities

On secure networks, even sophisticated cyber hackers must perform multiple steps to implement attacks on sensitive data and critical servers hidden behind layers of firewalls. Therefore, there is a need to study these attacks at a higher multi-stage level. Traditional taxonomy of cyber attacks focuses on analyzing the final stage and overall effects of an attack but, not the characteristics of an attack movement or `trajectory\u27 on a network. This work proposes to investigate trajectory similarities between multi-stage attacks, allowing for the characterization of both a hacker\u27s behavior and vulnerable attack paths within a network. Currently, Intrusion Detection Systems (IDS) report alerts to a network analyst when a malicious activity is suspected to have occurred on a network. Previous work in this field has used IDS alerts as evidence of multi-stage attacks, and has thus been able to group correlated alerts into cyber attack tracks. The main contribution of this work is to use a revised Longest Common Subsequence(LCS) algorithm to analyze attack tracks as trajectories. This allows a systematic analysis to determine which alert attributes within a track are of great value to the characterization of multi-stage attacks. The basic LCS algorithm, which looks for the longest common sequence in two strings of data, is extended to support the non-uniformity of alert data using a time windowing system. In addition, a normalization method will be applied to ensure that the attack track similarity measure is not adversely affected by differences in attack track length. By applying the revised LCS algorithm, attack trajectories defined in terms of various IDS alert attributes are analyzed. The results provide strong indicators of how multidimensional cyber attack trajectories can be used to differentiate attack tracks

Image Processing Applications in Real Life: 2D Fragmented Image and Document Reassembly and Frequency Division Multiplexed Imaging

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied. In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the Fourier domain. Finally, a Texas Instruments digital micromirror device (DMD) based implementation of FDMI is presented and results are shown. Chapter 2 discusses the problem of image reassembly which is to restore an image back to its original form from its pieces after it has been fragmented due to different destructive reasons. We propose an efficient algorithm for 2D image fragment reassembly problem based on solving a variation of Longest Common Subsequence (LCS) problem. Our processing pipeline has three steps. First, the boundary of each fragment is extracted automatically; second, a novel boundary matching is performed by solving LCS to identify the best possible adjacency relationship among image fragment pairs; finally, a multi-piece global alignment is used to filter out incorrect pairwise matches and compose the final image. We perform experiments on complicated image fragment datasets and compare our results with existing methods to show the improved efficiency and robustness of our method. The problem of reassembling a hand-torn or machine-shredded document back to its original form is another useful version of the image reassembly problem. Reassembling a shredded document is different from reassembling an ordinary image because the geometric shape of fragments do not carry a lot of valuable information if the document has been machine-shredded rather than hand-torn. On the other hand, matching words and context can be used as an additional tool to help improve the task of reassembly. In the final chapter, document reassembly problem has been addressed through solving a graph optimization problem