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

Integration of stereo vision system calibration and kinematic calibration for an autonomous kiwifruit harvesting system

Stereo vision system and manipulator are two major components of an autonomous fruit harvesting system. In order to raise the fruit-harvesting rate, stereo vision system calibration and kinematic calibration are two significant processes to improve the positional accuracy of the system. This article reviews the mathematics of these two calibration processes and presents an integrated approach for acquiring calibration data and calibrating both components of an autonomous kiwifruit harvesting system. The calibrated harvesting system yields good positional accuracy in the laboratory tests, especially in harvesting individual kiwifruit. However, the performance is not in line with the outcomes in the orchard field tests due to the cluster growing style of kiwifruit. In the orchard test, the calibrations reduce the fruit drop rate but it does not impressively raise the fruit harvesting rate. Most of the fruit in the clusters remain in the canopy due to the invisibility of the stereo vision system. After analyzing the existing stereo vision system, a future visual sensing system research direction for an autonomous fruit harvesting system is justified

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

Recovery voltage in transformer oil-paper insulation diagnosis

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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