Covaｒiate-invariant gait analysis for human identification（人識別を目的とする共変量不変歩行解析）

信州大学(Shinshu university)博士（工学)ThesisYEOH TZE WEI. Covaｒiate-invariant gait analysis for human identification（人識別を目的とする共変量不変歩行解析）. 信州大学, 2018, 博士論文. 博士（工学), 甲第692号, 平成30年03月20日授与.doctoral thesi

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future

PENGENALAN MANUSIA BERBASIS PADA SINGLE-GAIT MENGGUNAKAN METODE MODIFIKASI LATENT CONDITIONAL RANDOM FIELD (L-CRF)

Pengenalan gait merupakan salah satu bagian dari computer vision yang berfungsi untuk mengenali subjek (manusia) dengan jarak tertentu tanpa memperhatikan aspek biometrik seperti iris, wajah, dan sidik jari. Latent Conditional Random Field (L-CRF) merupakan salah satu algoritma pengenalan single-gait dengan hasil yang lebih baik.Walaupun hasil performansi akurasi subjek dengan kondisi berjalan normal (#NM) yang lebih baik, tapi masih terdapat masalah performansi akurasi terhadap kondisi berjalan lain seperti membawa tas (#BG) dan memakai jas (#CL). Modifikasi Latent Conditional Random Field (mL-CRF) merupakan salah satu metode yang masih berkaitan dengan L-CRF, tapi memiliki perbedaan pada parameter pairwise. Keunggulannya adalah hasil yang lebih baik dalam melatih dan menguji data dari domain yang identik. Penelitian ini menggunakan silhouette frames pada data set CASIA gait database B yang berisi 124 subjek dengan 110 sequence tiap subjek. Proses pengolahan data mLCRF dilakukan berdasarkan sampel training (LT74 & MT62) dan 11 sudut pengamatan yang akan dibandingkan dengan L-CRF tanpa modifikasi, serta penelitian-penelitian sebelumnya. Pada penelitian ini, LT74 pada mL-CRF merupakan sampel training yang paling baik yang menghasilkan peningkatan akurasi sebesar 0,89% (#NM), 1,32% (#BG), 1,54% (#CL) terhadap LCRF tanpa modifikasi

A Survey of Deep Learning Solutions for Multimedia Visual Content Analysis

The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today's world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artifacts in multimedia. It surveys the recent, authoritative, and the best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. This paper also discusses the challenges that the DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis

Development of a non-invasive motion capture system for swimming biomechanics

Sports researchers and coaches currently have no practical tool that can accurately and rapidly measure the 3D kinematics of swimmers. Established motion capture methods in biomechanics are not well suited for underwater use, either because they i) are not accurate enough (like depth-based systems, or the visual hull), ii) would impair the movement of swimmers (like sensor- and marker-based systems), or iii) are too time consuming (like manual digitisation). The ideal for swimming motion capture would be a markerless motion capture system that only requires a few cameras. Such a system would automatically extract silhouettes and 2D joint locations from the videos recorded by the cameras, and fit a generic 3D body model to these constraints. The main challenge in developing such a system for swimming motion capture lies in the development of algorithms for silhouette extraction and 2D pose detection (i.e., localisation of joints in image coordinates), which need to perform well on images of swimmers—a task that currently available algorithms fail. The aim of this PhD was the development of such algorithms. Existing datasets do not contain images of swimmers, making it impossible to train algorithms that would perform well in this domain. Therefore, during the PhD two datasets of images of swimmers were constructed and hand-labelled: one, called Scylla, for silhouette extraction (3,100 images); and one, called Charybdis, for 2D pose detection (8,000 images). Scylla and Charybdis are the first datasets developed specifically for training algorithms to perform well on images of swimmers. Indeed, using these datasets, two algorithms were developed during this PhD: FISHnet, for silhouette extraction; and POSEidon, for 2D pose detection. The novelty of FISHnet (which outperformed state-of-the-art algorithms on Scylla) lies in its ability to predict outputs at the same resolution as the inputs, allowing it to reconstruct fine-grained silhouettes. The novelty of POSEidon lies in its unique structure, which allows it to directly regress the x and y coordinates of joints without needing heatmaps. POSEidon is almost as accurate as humans at locating the spinal joints of swimmers, which are essential constraints onto which to fit 3D models. Using these two algorithms, researchers will, in the future, be able to assemble a markerless motion capture system for swimming, which will contribute to improving our understanding of swimming biomechanics, as well as providing coaches a tool with which to monitor the technique of swimmers

Deep Learning-Based Human Pose Estimation: A Survey

Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 240 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. We also provide a regularly updated project page: \url{https://github.com/zczcwh/DL-HPE