Symbolic Modelling of Dynamic Human Motions

Numerous psychological studies have shown that humans develop various stylistic patterns of motion behaviour, or dynamic signatures, which can be in general, or in some cases uniquely, associated with an individual. In a broad sense, such motion features provide a basis for non-verbal communication, or body language, and in more specific circumstances they combine to form a Dynamic Finger Print (DFP) of an individual, such as their gait, or walking pattern. A new modelling and classification approach for spatiotemporal human motions is proposed, and in particular the walking gait. The movements are obtained through a full body inertial motion capture suit, allowing unconstrained freedom of movements in natural environments. This involves a network of 16 miniature inertial sensors distributed around the body via a suit worn by the individual. Each inertial sensor provides (wirelessly) multiple streams of measurements of its spatial orientation, plus energy related: velocity, acceleration, angular velocity and angular acceleration. These are also subsequently transformed and interpreted as features of a dynamic biomechanical model with 23 degrees of freedom (DOF). This scheme provides an unparalleled array of ground-truth information with which to further model dynamic human motions compared to the traditional optically-based motion capture technologies. Using a subset of the available multidimensional features, several successful classification models were developed through a supervised machine learning approach. This chapter describes the approach, methods used together with several successful outcomes demonstrating: plausible DFP models amongst several individuals performing the same tasks, models of common motion tasks performed by several individuals, and finally a model to differentiate abnormal from normal motion behaviour. Future developments are also discussed by extending the range of features to also include the energy related attributes. In doing so, valuable future extensions are also possible in modelling, beyond the objective pose and dynamic motions of a human, to include the intent associated with each motion. This has become a key research area for the perception of motion within video multimedia, for improved Human Computer Interfaces (HCI), as well as its application directions to better animate more realistic behaviours for synthesised avatars

Enhancing Multimedia Search Using Human Motion

Over the last few years, there has been an increase in the number of multimedia-enabled devices (e.g. cameras, smartphones, etc.) and that has led to a vast quantity of multimedia content being shared on the Internet. For example, in 2010 thirteen million hours of video uploaded to YouTube (http://www.youtube.com). To usefully navigate this vast amount of information, users currently rely on search engines, social networks and dedicated multimedia websites (such as YouTube) to find relevant content. Efficient search of large collections of multimedia requires metadata that is human-meaningful, but currently multimedia sites generally utilize metadata derived from user-entered tags and descriptions. These are often vague, ambiguous or left blank, which makes search for video content unreliable or misleading. Furthermore, a large majority of videos contain people, and consequently, human movement, which is often not described in the user entered metadata

Embedded lossless audio coding using linear prediction and cascade coding

This thesis studies the techniques and feasibility of embedding a perceptual audio coder within a lossless compression scheme. The goal is to provide for two step scalability in the resulting bitstream, where both a perceptual version of the audio signal and a lossless version of the same signal are provided in the one bitstream. The focus of this thesis is the selection of the perceptual coder to be used as the perceptual base layer and the techniques to be used to compress the lossless layer by using backward linear prediction followed by entropy coding. The perceptual base layer used is MPEG-4 AAC, chosen based on entropy measurements of the residual signal. Results of the work in this thesis show that the embedded lossless coding scheme could achieve an average compression ratio of only 6% larger compared to lossless only coding. Performing decorrelation on the AAC residual signal by means of backward linear predictive coding and measuring the entropy of the resulting LPC residual signal of various orders revealed that an 8% decrease in coding rate is achievable using 15th order prediction. Furthermore, this thesis also investigates an entropy coding technique known as cascade coding which is originally designed to compress hydroacoustic image data and is modified to compress audio data. Cascade coding is an entropy coding technique that uses multiple cascaded stages where each stage codes a specific range of integers and is used to perform entropy coding of the backward linear prediction residual signal. The cascade coding technique explored in this thesis includes using a frame based approach and trained codebooks

Searching and describing human motion

The amount of media being uploaded to the Internet is growing at an incredible rate. As an illustration, approximately 75 hours of video are uploaded to Youtube each minute, where approximately 30% of the videos contain human motion such as sport or music video. Consequently, new techniques and methods to search and describe contents related to human motion are sorely needed, since current search techniques mainly depend on user-supplied tags, which are often ambiguous and subjective when those tags are used to describe human motion. For example, a video containing “John Doe running and jumping into a lake” can be tagged as “John Doe”, “lake”, “running and jumping”, “funny video”, etc. Being able to search for a specific motion has many applications. For example, searching for a specific movement in a sport in order to improve a person’s sporting performance by comparing to that of a professional athlete’s using automatically extracted movement features (such as a famous golfer’s swing, a famous tennis player’s forehand, etc.). This scenario will be possible if a method to objectively describe human motion existed. Searching human motion would be as natural as recording a motion and using it as yet another search term without having to think about the subjectivity of user-supplied tags and how someone else would “describe” that motion. To achieve this, three things are required: a new multimedia communication format (since currently popular search techniques predominantly use simple text terms), a new human motion description language (since an objective and consistent method to describe human motion is also required), and feature extraction and matching technique for human motion search applications. To communicate advanced multimedia queries, Multimedia Query Format (MQF) is presented in this thesis. MQF is a communication format for a structured multimedia search that goes beyond current text-based search currently in popular use. Instead of restricting itself to one particular multimedia description format, MQF was designed to allow the use of any number of current or future description standards, with advanced features for search such as logical operators, query-by-example, extensibility, and simplicity. MQF is also shown to work well with Fragment Request Unit (FRU) and Fragment Update Unit (FUU), which are MPEG standards that enable selective synchronization of two XML documents over a network. Using FRU and FUU, MQF is shown to be able to perform “Query Streaming”, which is a continuously updatable multimedia query method that is suitable for use in mobile devices with limited resources. The work performed in MQF was also proposed to MPEG during the MPEG-7 Query Format standardization effort, where concepts introduced by MQF were contributed to the discussions, refinements, and validations during the MPEG standardization process. To describe human motion objectively and accurately, Human Motion Markup Language (HMML) is presented in this thesis. HMML is a human motion description language that was designed to be able to describe human motion in three dimensions (sagittal, coronal, and transverse planes) to facilitate human motion centric search. Another design goal of HMML is to enable human motion search by utilizing MQF as the communication format, where HMML can be used in conjunction with existing multimedia description standard such as MPEG-7 and Dublin Core to provide a more complete description of a desired media not currently possible today. Key features of HMML includes human readability, simplicity, and searchability. To extract this objective human motion description, a method to automatically extract HMML motion description from 3D motion capture data is also presented. This method involves “partial reconstruction” of the human body, i.e., each of the major limb such as the arms and the legs are reconstructed from 3D data independently. By not reconstructing the body as a whole, each limb becomes a separate entity that can be described independent of other limbs in an objective manner. Consequently, applications searching for a walking motion with the leg movements to serve as the query term will also match walking and waving, walking and dribbling, etc., providing a fine-grained method for motion search. Experiments were performed to determine the consistency of the extracted symbol sequences using walking, running, and sneaking motions, where it was found that the extracted symbols are consistent even when the symbols were extracted from people of varying height and movement patterns. Also, the optimal motion duration and detail level of the extracted symbol sequence were investigated to utilize the symbol sequences in a motion matching application

Embedded lossless audio coding using linear prediction and cascade

Embedded lossless audio coding is a technique for embedding a perceptual audio coding bitstream within a lossless audio coding bitstream. This paper provides an investigation into a lossless embedded audio coder based on the AAC coder and utilising both backward Linear Predictive Coding (LPC) and cascade coding. Cascade coding is a technique for entropy coding of large dynamic range integer sequences that has the advantage of simple implementation and low complexity. Results show that employing LPC in an embedded architecture achieves approximately an 8% decrease in the coding rate. The overall compression performance of cascade coding closely follows Rice coding, a popular entropy coding method for lossless audio. It is also shown that performance can be further improved by incorporating a start of the art lossless coder into the proposed embedded coder

Investigation of corrosion behaviour of magnesium in aqueous solutions

The paper present.. the corrosion data of ma~nesium metal in different aqueous solutions such as NHl~l, Naf and NaCl04 of concentrations ran~ing from 0.1 to 2.0 M. Pola.-isation and optical methods have been used to evaluate the cOITosion paraDlt'ters and surface properties. The dependence of the open circuit potential, cOlTosion potential, corrosion currt'nt and Tafel slopes on the nature and concentmtion of the electrolytes has been examined. Corrosion current increases linearly with concentration of the electrolyte in all the media. The findinJ!;s further infer the corrosivity of the anions decrease in the ordt'r as CI-> r > no:;. The naturt' of the corrosion products formed on the surface as well as lIlorphnlogical features of the specimt'll nhtained from XRD and SEM techniques are also discussed in this communicatioII

Query streaming for multimedia query by content from mobile devices

Formulating and processing of multimedia queries using mobile devices presents many challenges. This is due to the limitations of the devices themselves and the cost of the bandwidth involved in transmitting multimedia data between servers and devices. In this paper we propose a novel approach: “query streaming” which uses Reverse Polish Notation to perform multimedia query-by-example on a mobile device and server. An important advantage of query streaming is the ability to perform a query within the previous result set. To solve the problem of limited resources, the concept of result set examination using Fragment Request Units and Fragment Update Units is also explored. MPEG BiM compression is performed on the communication messages to further minimize transmission requirements

Wiener Studien 134 - Rezensionen

Automatic generation of metadata is an important component of multimedia search-by-content systems as it both avoids the need for manual annotation as well as minimising subjective descriptions and human errors. This paper explores the automatic attachment of basic descriptions (or dasiaTagspsila) to human motion held in a motion-capture database on the basis of a dynamic time warping (DTW) approach. The captured motion is held in the Acclaim ASF/AMC format commonly used in game and movie motion capture work and the approach allows for the comparison and classification of motion from different subjects. The work analyses the bone rotations important to a small set of movements and results indicate that only a small set of examples is required to perform reliable motion classification

Toward human motion search using fingerprinting

This paper investigates a `fingerprinting\u27 technique for describing human motion sequences. This work shows that human motion fingerprints can facilitate the search of human motion within large databases, similar to the fingerprinting approach used for the search of audio and image databases. This paper investigates the extraction of a reliable set of features from human motion capture data sequences that can be combined to generate a unique fingerprint. Results show that the fingerprints could be used to reliably differentiate between unique motions

Small business lending in the United States

This paper proposes a novel limb-based technique for semantic description of motion capture data. The goal is to create a motion segmentation and classification technique that is easily extensible by recognizing the actions of a limb instead of the whole body. This provides a highly detailed metadata that can be extended as needed to include additional motion classes by either adding a new limb submotion or by defining a new full-body motion class that combines existing known limb movements. The results of the initial implementation for annotating the leg movements (forward and backward) of walking and running show that such a system is feasible, with annotation accuracy of more than 98%