Two-Stream RNN/CNN for Action Recognition in 3D Videos

The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes. Despite advances in sensing, in particular related to 3D video, the methodologies to process the data are still subject to research. We demonstrate superior results by a system which combines recurrent neural networks with convolutional neural networks in a voting approach. The gated-recurrent-unit-based neural networks are particularly well-suited to distinguish actions based on long-term information from optical tracking data; the 3D-CNNs focus more on detailed, recent information from video data. The resulting features are merged in an SVM which then classifies the movement. In this architecture, our method improves recognition rates of state-of-the-art methods by 14% on standard data sets.Comment: Published in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Disturbance Grassmann Kernels for Subspace-Based Learning

In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers misled by disturbed instances. Thus we propose considering all potential disturbance of subspaces in learning processes to obtain more robust classifiers. Firstly, we derive the dual optimization of linear classifiers with disturbance subject to a known distribution, resulting in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into two kinds of disturbance, relevant to the subspace matrix and singular values of bases, with which we extend the Projection kernel on Grassmann manifolds to two new kernels. Experiments on action data indicate that the proposed kernels perform better compared to state-of-the-art subspace-based methods, even in a worse environment.Comment: This paper include 3 figures, 10 pages, and has been accpeted to SIGKDD'1

Sistem Peringatan Tersemat untuk Pengemudi Mengantuk

Pendeteksian Driver Drowsiness (DDD) merupakan teknologi keselamatan kendaraan penting yang dirancang untuk mencegah kecelakaan akibat kantuk pengemudi. Dalam penelitian ini, pendekatan baru diajukan menggunakan model jaringan saraf konvolusi (CNN) ringan yang terdiri dari 44.853 parameter. Berkat ke ringanannya, model ini bekerja secara efisien bahkan pada perangkat dengan sumber daya terbatas. Hasil percobaan menunjukkan kinerja kompetitif model ini dibandingkan dengan model yang ada dengan ukuran input dan jumlah parameter yang lebih besar. Dalam hal akurasi, metode ini mencapai akurasi sebesar 92,06% pada dataset Curtin Emotion Wheels (CEW) yang mengesankan. Bahkan dalam kondisi pencahayaan yang sulit, performa model ini tetap luar biasa jika digabungkan dengan kamera termal. Secara khusus, model ini mencapai akurasi yang luar biasa sebesar 95,10% pada jarak kamera 0,3-meter dari wajah pengemudi. Selain itu, metode ini memiliki karakteristik kecepatan yang sangat baik, sehingga cocok digunakan pada perangkat tertanam. Kecepatan rata-rata perangkat Raspberry Pi 4 diperkirakan mencapai 5 frames per detik (FPS). Hal ini menunjukkan kepraktisan dan kelayakan penerapan pendekatan ini dalam skenario waktu nyata, yang semakin meningkatkan keselamatan pengemudi

Food intake gesture monitoring system based-on depth sensor

Food intake gesture technology is one of a new strategy for obesity people managing their health care while saving their time and money. This approach involves combining face and hand joint point for monitoring food intake of a user using Kinect Xbox One camera sensor. Rather than counting calories, scientists at Brigham Young University found dieters who eager to reduce their number of daily bites by 20 to 30 percent lost around two kilograms a month, regardless of what they ate [1]. Research studies showed that most of the methods used to count bite are worn type devices which has high false alarm ratio. Today trend is going toward the non-wearable device. This sensor is used to capture skeletal data of user while eating and train the data to capture the motion and movement while eating. There are specific joint to be capture such as Jaw face point and wrist roll joint. Overall accuracy is around 94%. Basically, this increase in the overall recognition rate of this system

Non-parametric hidden conditional random fields for action classification

Conditional Random Fields (CRF), a structured prediction method, combines probabilistic graphical models and discriminative classification techniques in order to predict class labels in sequence recognition problems. Its extension the Hidden Conditional Random Fields (HCRF) uses hidden state variables in order to capture intermediate structures. The number of hidden states in an HCRF must be specified a priori. This number is often not known in advance. A non-parametric extension to the HCRF, with the number of hidden states automatically inferred from data, is proposed here. This is a significant advantage over the classical HCRF since it avoids ad hoc model selection procedures. Further, the training and inference procedure is fully Bayesian eliminating the over fitting problem associated with frequentist methods. In particular, our construction is based on scale mixtures of Gaussians as priors over the HCRF parameters and makes use of Hierarchical Dirichlet Process (HDP) and Laplace distribution. The proposed inference procedure uses elliptical slice sampling, a Markov Chain Monte Carlo (MCMC) method, in order to sample optimal and sparse posterior HCRF parameters. The above technique is applied for classifying human actions that occur in depth image sequences – a challenging computer vision problem. Experiments with real world video datasets confirm the efficacy of our classification approach

Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented

Reconocimiento de actividades humanas mediante SVM semisupervisado y modelos ocultos de Markov

Automatic human activity recognition is an area of interest for developing health, security, and sports applications. Currently, it is necessary to develop methods that facilitate the training process and reduce the costs of this process. This paper explores a methodology to classify human physical activities in a semi-supervised paradigm. With this approach, it is possible to reduce the number of labels necessary to train the learning model and the complexity of this process. This process begins by deducting the number of micro-movements or sub-movements where the data should be grouped and assigning the label through a clustering technique. We perform this procedure for a specific group of micro-movements whose label is unknown. Later, the classification process starts by using two methods, a Support Vector Machine (SVM) that identifies the micro-movements and a Markov Hidden Model that detects the human physical activity as a function of sequences. The results show that with a percentage of 80 % of the known labels, we achieved outcomes like the supervised paradigms found in the literature. This facilitates training these learning models by reducing the number of examples requiring labels and reduces the economic costs, which is one of the significant limitations of machine learning processes.El reconocimiento automático de la actividad humana es un área de interés para el desarrollo de aplicaciones en salud, seguridad y deportes. Actualmente, es necesario desarrollar métodos que faciliten el proceso de entrenamiento y reduzcan los costos de este proceso. Este trabajo explora una metodología para clasificar actividades físicas humanas en un paradigma semi-supervisado. Con este enfoque, es posible reducir el número de etiquetas necesarias para entrenar el modelo de aprendizaje y la complejidad de este proceso. Este proceso comienza deduciendo el número de micro-movimientos o submovimientos en los que deben agruparse los datos y asignando la etiqueta mediante una técnica de clustering. Realizamos este procedimiento para un grupo específico de micro-movimientos cuya etiqueta se desconoce. Posteriormente, se inicia el proceso de clasificación utilizando dos métodos, una Máquina de Vectores Soportados (SVM) que identifica los micro-movimientos y un Modelo Oculto de Markov que detecta la actividad física humana en función de secuencias. Los resultados muestran que con un porcentaje del 80 % de las etiquetas conocidas, se consigue resultados como los paradigmas supervisados encontrados en la literatura. Esto facilita el entrenamiento de estos modelos de aprendizaje al reducir el número de ejemplos que requieren etiquetas y reduce los costes económicos, que es una de las limitaciones significativas de los procesos de aprendizaje automático