A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

Human Perambulation as a Self Calibrating Biometric

This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios

PENGEMBANGAN ALGORITMA SKELETONISASI DAN EKSTRAKSI FITUR NON-INTRUSIVE PADA GAYA BERJALAN MANUSIA SECARA REAL TIME

Penelitian analisis gaya berjalan (gait) manusia yang baik terus berkembang seiring dengan perkembangan aplikasi pengolahan citra dalam berbagai bidang serta perkembangan teknologi komputer dan kamera video. Bidang kedokteran dan bidang olah raga adalah bidang yang terkait langsung dengan penelitian ini. Dalam bidang kedokteran penelitian ini dapat membantu dokter dalam mendiagnosis pasien yang mengalami kelainan berjalan, merancang program rehabilitasi dan desain prostetik. Pada bidang olah raga hasil penelitian ini dapat digunakan pelatih untuk mengidentifikasi kesalahan-kesalahan gerakan pada atlet dan mengajarkan teknik terbaik, aman dan efektif sehingga dapat meningkatkan performa atlet tersebut. Penelitian ini mengusulkan metode dan algoritma analisis gaya berjalan manusia serta pengembangan prototipe perangkat lunak. Metode yang dikembangkan berupa analisis gaya berjalan secara real-time dan non-intrusive (tanpa menggunakan penanda ataupun intervensi operator). Metode ini didukung oleh algoritma akuisis citra, algoritma segmentasi, algoritma skeletonisasi dan algoritma ekstraksi fitur gerakan. Tahap pertama analisis gaya berjalan seseorang adalah akuisisi objek (seseorang yang sedang berjalan) menggunakan webcam Logitech Quickcam Pro High Definition 9000 secara real-time. Tahap kedua adalah segmentasi yang terdiri dari pra proses dan pembentukan siluet. Pada pra proses, setiap frame gait yang dihasilkan dari akuisisi gaya berjalan manusia diproses menggunakan metode background subtraction. Selanjutnya dilakukan pembentukan siluet tubuh manusia melalui proses filtering, thresholding, dilasi, erosi, dan inversi. Tahap ketiga adalah proses skeletonisasi, yakni proses pembentukan skeleton menggunakan metode thinning. Tahap terakhir yang dilakukan adalah proses ekstraksi fitur, yaitu proses pengukuran parameter jarak kedua kaki menggunakan perhitungan geometri ruang untuk memperoleh koordinat dari setiap frame citra skeleton pada bagian kedua kaki manusia. Kelebihan dari teknik yang digunakan ini dibandingkan teknik lainnya adalah keseluruhan proses tersebut dilakukan dengan metode non-intrusive, dimana objek tidak perlu menggunakan penanda (marker) dan operator tidak perlu memberikan tanda secara manual pada skeleton untuk memudahkan proses ekstraksi, karena sistem secara otomatis dapat mendeteksi titik-titik yang diperlukan untuk proses ekstraksi fitur. Berdasarkan hasil analisis dan pengujian yang telah dilakukan dapat disimpulkan bahwa algoritma akuisisi dapat mencuplik citra gait manusia secara real-time dengan kecepatan 30 citra/detik. Algoritma segmentasi mampu menghasilkan siluet yang relatif sama dengan bentuk tubuh orang yang terekam. Algoritma skeletonisasi telah mampu menghasilkan skeleton dari citra siluet dan memudahkan proses ekstraksi fitur. Algoritma ekstraksi fitur yang dikembangkan berhasil melakukan perhitungan fitur-fitur gait manusia secara otomatis dan tanpa interfensi operator atau bersifat non-intrusive dalam waktu yang relatif cepat. Tingkat akurasi dari metode dan algoritma yang dikembangkan mencapai 97.75%

Principal component analysis for human gait recognition system

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject

Improved Gait Classification with Different Smoothing Techniques

Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification

GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition

Gait recognition is an emerging biological recognition technology that identifies and verifies individuals based on their walking patterns. However, many current methods are limited in their use of temporal information. In order to fully harness the potential of gait recognition, it is crucial to consider temporal features at various granularities and spans. Hence, in this paper, we propose a novel framework named GaitGS, which aggregates temporal features in the granularity dimension and span dimension simultaneously. Specifically, Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturing the micro-motion and macro-motion information at the frame level and unit level respectively. Moreover, we present Multi-Span Feature Learning (MSFL) module to generate global and local temporal representations. On three popular gait datasets, extensive experiments demonstrate the state-of-the-art performance of our method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0% (+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. The source code will be released soon.Comment: 14 pages, 6 figure

A review of vision-based gait recognition methods for human identification

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field

PENGENALAN INDIVIDU BERDASARKAN GAIT MENGGUNAKAN SINGULAR VALUE DECOMPOSITION DAN JARINGAN SYARAF TIRUAN BACK PROPAGATION

ABSTRAK Pengenalan individu berdasarkan gait memiliki kelebihan dalam hal sifatnya yang tidak mudah untuk ditiru dan diubah. Gait atau cara berjalan setiap individu bisa dikatakan unik karena setiap individu umumya memiliki cara berjalan yang unik. Kelebihan lainnya biometrik dari gait dapat bekerja dalam jarak jauh. Pengenalan individu melalui webcam dengan masukan berupa video dapat menjadi alternatif lain untuk pengenalan individu biometrik selain dengan metode pengenalan biometrik lainnya seperti sidik jari dan iris mata. Tugas akhir ini mencoba mengimplementasikan metode reduksi data singular value decomposition (SVD) dan metode klasifikasi jaringan syaraf tiruan (JST) back propagation dalam identifikasi individu berdasarkan gait. SVD berfungsi untuk mendekomposisi ciri gait yang dihasilkan dengan tujuan mereduksi jumlah data ciri dan mengambil hanya nilai penting dari ciri tersebut. Proses selanjutnya dilakukan klasifikasi menggunakan JST back propagation. Dengan mencari kombinasi parameter-parameter backpropagation terbaik pada nilai epoch, learning rate, jumlah neuron hidden layer, dan target mean square error rate (MSE) dengan melakukan trial and error hingga menemukan nilai persentase akurasi optimal pada pengujian pengenalan individu. Keluaran dari sistem ini adalah ketepatan dalam mengenali suatu objek berjalan. Ketepatan dari sistem ini dinilai dari persentase ketepatan mengenali individu berjalan. Singular value decomposition dengan jaringan syaraf tiruan back propagation memiliki pengenalan ciri yang cukup baik pada kasus pengenalan individu berdasarkan gait karena terbukti mampu memberikan nilai akurasi sebesar 90%. Akurasi ini dicapai pada training JST back propagation dengan parameter jumlah neuron hidden layer 10, dengan epoch 1000, target MSE 1e-20, dan dengan parameter learning rate 0.01. Kata Kunci: Gait, SVD, Jaringan Syaraf Tiruan Back propagation. ABSTRACT Gait based recognition’s features has advantages to be a recognition system, because it is not easy to imitate and modified. In the case of gait has unique feature, because it has unique style from each individual. Another advantage of gait biometrics can work over long distances. Recognition individuals via webcam with input in the form of video can be an alternative to the recognition biometric individuals other than the recognition biometric methods such as fingerprint and iris. This final task is try to implement a data reduction method using singular value decomposition (SVD) and artificial neural network (ANN) back propagation for classification methods in the recognition individuals based on gait. SVD is used to decompose the gait characteristics produced with the aim of reducing the amount of data characteristics and take only the importance of these traits. The process will then be carried out classification using back propagation neural network. By looking for the combination of back propagation best parameters on the value of the epoch, learning rate, the number of hidden layer neurons, and mean square error rate (MSE) target by doing trial and error to find the optimal value of the percentage of accuracy in testing individual recognition. The output of this system is accuracy in recognizing an object walk. The accuracy of this system will be judged by the percentage of accuracy of recognizing individuals walking. Singular value decomposition with back propagation neural network has the characteristics of a pretty good introduction to the case of an individual based gait recognition because it proved able to deliver a 90% accuracy rate. This accuracy is achieved in training back propagation neural network with parameters of hidden layer neuron number 10, with the epoch 1000, target MSE 1e-20, and the learning rate parameter 0.01. Keywords: Gait, SVD, Back propagation Neural Network