53 research outputs found
Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the fixed viewpoint assumption and present a novel and simple framework to recognize and classify human activities from uncalibrated monocular video source from any viewpoint. The proposed framework comprises two stages: 3D human pose estimation and human activity recognition. In the pose estimation stage, we estimate 3D human pose by a simple search-based and tracking-based technique. In the activity recognition stage, we use Nearest Neighbor, with Dynamic Time Warping as a distance measure, to classify multivariate time series which emanate from streams of pose vectors from multiple video frames. We have performed some experiments to evaluate the accuracy of the two stages separately. The encouraging experimental results demonstrate the effectiveness of our framework
Evaluation of Deep Neural Network and alternating decision tree for kiwifruit detection
Robotic kiwifruit harvesting systems are currently being introduced to improve the reliability and farming yields of kiwifruit harvesting operations. Machine learning is widely used to carry out the visual detection tasks required of such systems. This paper specifically compares two types of machine learning algorithms: the multivariate alternating decision tree and deep learning based kiwifruit classifiers. The purpose of the study is to investigate the cost of implementation against the classification performance. Thus, discussion is centred around computational cost and its impacts on the overall system architecture. We found that the traditional decision tree classifiers can achieve comparable classification performance at a fraction of the cost and complexity, providing robust and cost-effective instrument design
Maximal Associated Regression: A nonlinear extension to Least Angle Regression
This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates. For each predictor, MAR selects between a linear or additive fit as determined by the dataset. The proposed algorithm is an adaptation of Least Angle Regression (LARS) and retains its efficiency in building sparse models. Constrained penalized splines are used to generate smooth nonlinear transformations for the additive fits. A monotonically constrained extension of MAR (MARm) is also introduced in this paper to fit isotonic regression problems. The proposed algorithms are validated on both synthetic and real datasets. The performances of MAR and MARm are compared against LARS, Generalized Linear Models (GLM), and Generalized Additive Models (GAM) under the Gaussian assumption with a unity link function. Results indicate that MAR-type algorithms achieve a superior subset selection accuracy, generating sparser models that generalize well to new data. MAR is also able to generate models for sample deficient datasets. Thus, MAR is proposed as a valuable tool for subset selection and data exploration, especially when a priori knowledge of the dataset is unavailable
Roadmap on signal processing for next generation measurement systems
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System
Multi-parameter, impulsive effects and positive periodic solutions of first-order functional differential equations
Recovery voltage in transformer oil-paper insulation diagnosis
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Robust autopilot design with maximum stability radius
This paper presents the robust control design for aircraft autopilot. The controller provides a maximum stability radius to the closed-loop system. The technique uses the notion of complex stability radius and the Linear Matrix Inequalities (LMI) to obtain the feedback controller gain. The technique is illustrated on a feedback controller of an autopilot system of a twin engine civil aircraft. The results is compared to those that are obtained using a linear quadratic regulator (LQR) technique
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