An efficient maximum power point tracking (MPPT) method plays an important role to improve the efficiency of a photovoltaic (PV) generation system. This study provides an extensive review of the ...current status of MPPT methods for PV systems which are classified into eight categories. The categorisation is based on the tracking characteristics of the discussed methods. The novelty of this study is that it focuses on the key characteristics and eleven selection parameters of the methods to make a comprehensive analysis, which is not considered together in any review works so far. Again, the pros and cons, classification and immense comparison among them described in this study can be used as a reference to address the gaps for further research in this field. A comparative review in tabular form is also presented at the end of the discussion of each category to evaluate the performance of these methods, which will help in selecting the appropriate technique for any specific application.
Autonomous pilot is crucial in integrally promoting the autonomy of an unmanned surface vehicle (USV). However, the integration mechanism of decision and control is still unclear within the entire ...autonomy. In this paper, by organically bridging path planning and tracking, an autonomous pilot framework with waypoints generation, path smoothing and policy guidance of a USV in congested waters is established, for the first time. Incorporating elite and diversity operations into the genetic algorithm (GA), an elite-duplication GA (EGA) strategy is devised to optimally generate sparse waypoints in a constrained space. The B-spline technique is further deployed to make flexibly smooth interpolation facilitating path smoothing supported by optimal sparse-waypoints. Seamlessly bridged by the parametric smooth path, deep reinforcement learning (DRL) technique is resorted to continuously extract in-depth pilotage policies, i.e., mappings from path tracking errors, collision risks and control constraints to continuous control forces/torques. Eventually, the entire spline-bridged EGA-DRL (SED) framework merits autonomous global-pilotage and local-reaction in an organically modular manner. Comprehensive validations and comparisons in various real-world geographies demonstrate the effectiveness and superiority of the proposed SED autonomous pilot framework.
Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. To learn a robust tracker for VAT, in this ...article, we propose a novel adversarial reinforcement learning (RL) method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In the mechanism, the tracker and target, viewed as two learnable agents, are opponents and can mutually enhance each other during the dueling/competition: i.e., the tracker intends to lockup the target, while the target tries to escape from the tracker. The dueling is asymmetric in that the target is additionally fed with the tracker's observation and action, and learns to predict the tracker's reward as an auxiliary task. Such an asymmetric dueling mechanism produces a stronger target, which in turn induces a more robust tracker. To improve the performance of the tracker in the case of challenging scenarios such as obstacles, we employ more advanced environment augmentation technique and two-stage training strategies, termed as AD-VAT+. For a better understanding of the asymmetric dueling mechanism, we also analyze the target's behaviors as the training proceeds and visualize the latent space of the tracker. The experimental results, in both 2D and 3D environments, demonstrate that the proposed method leads to a faster convergence in training and yields more robust tracking behaviors in different testing scenarios. The potential of the active tracker is also shown in real-world videos.
This article considers the tracking of elliptical extended targets parameterized by center, orientation, and semiaxes. The focus of this article lies on the fusion of extended target estimates, e.g., ...from multiple sensors, by handling the ambiguities in this parameterization and the unclear meaning of the mean square error. For this purpose, we introduce a novel Bayesian framework for elliptic extent estimation and fusion based on two new concepts: 1) A probability density function for ellipses called random ellipse density which incorporates the ambiguities that come with the ellipse parameterization, and 2) the minimum mean Gaussian Wasserstein (MMGW) estimate, which is optimal with respect to the squared Gaussian Wasserstein (GW) distance-A suitable distance metric on ellipses. We develop practical algorithms for ellipse fusion and approximating the MMGW estimate. Different implementations, e.g., based on Monte Carlo simulation, are introduced and compared to state-of-the-art methods, highlighting the benefits of estimators tailored to the GW distance.
This paper proves the uncertainty bound for the multiple Gaussian functions, termed multiple Gaussians Uncertainty (MGU), which significantly generalizes the uncertainty principle for the single ...Gaussian function. First, as a theoretical contribution, we prove that the momentum (velocity) and position for the sum of multiple Gaussians wave function are theoretically bounded. Second, as for a practical application, we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework. By integrating MGU with a given object tracker, we demonstrate that uncertainty principle can provide remarkable robustness in tracking. Extensive experiments are done to show that the proposed MGU can significantly help base trackers overcome the object drifting and reach state-of-the-art results.
Accurate perception of the driving environment is a key technology for intelligent vehicles. Given some critical problems such as low robustness, low detection precision, difficulty in actual ...deployment, we propose a local environment model (LEM) based on multi-sensor fusion technology through Lidar, millimeter-wave (MMW) radar, camera, and ultrasonic radar. The local environment model mainly consists of the drivable area and the dynamic target list. The drivable area is extracted by the ground gradient threshold algorithm. Based on it, we propose an effective trim algorithm to make the drivable area model more practical. Furthermore, low-cost ultrasonic radars are deployed to compensate for the blind area of Lidar. The dynamic target list is established by local tracking and global tracking in the forward area. Kalman filter and converted measurement Kalman filter (CMKF) are adopted in the local tracking of Lidar, camera, and MMW radar. In the global tracking, the global nearest neighbor (GNN) algorithm is used for data association and the optimal distributed estimation fusion (ODEF) algorithm is used for sensor fusion. To improve the robustness of tracking, we use an assignment method to better exploit sensor performance. Finally, the vehicle experiment is carried out in the campus environment. Experimental results indicate that the proposed algorithm can avoid the false detection of the drivable area and realize real-time multi-target dynamic tracking. Therefore, the robustness and accuracy of the local environment model is verified.
To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be ...selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when encountering challenging tracking scenarios such as heavy occlusion and fast motion. In particular, if a tracker drifts, errors will be accumulated and would further make response scores estimated by the tracker unreliable in future frames. To address this issue, we propose to maintain multiple tracking trajectories and apply beam search strategy for visual tracking, so that the trajectory with fewer accumulated errors can be identified. Accordingly, this paper introduces a novel multi-agent reinforcement learning based beam search tracking strategy, termed BeamTracking. It is mainly inspired by the image captioning task, which takes an image as input and generates diverse descriptions using beam search algorithm. Accordingly, we formulate the tracking as a sample selection problem fulfilled by multiple parallel decision-making processes, each of which aims at picking out one sample as their tracking result in each frame. Each maintained trajectory is associated with an agent to perform the decision-making and determine what actions should be taken to update related information. More specifically, using the classification-based tracker as the baseline, we first adopt bi-GRU to encode the target feature, proposal feature, and its response score into a unified state representation. The state feature and greedy search result are then fed into the first agent for independent action selection. Afterwards, the output action and state features are fed into the subsequent agent for diverse results prediction. When all the frames are processed, we select the trajectory with the maximum accumulated score as the tracking result. Extensive experiments on seven popular tracking benchmark datasets validated the effectiveness of the proposed algorithm.
Reading is one of the most common everyday activities, yet research elucidating how affective influence reading processes and outcomes is sparse with inconsistent results. To investigate this ...question, we randomly assigned participants (N = 136) to happiness (positive affect), sadness (negative affect), and neutral video-induction conditions prior to engaging in self-paced reading of a long, complex science text. Participants completed assessments targeting multiple levels of comprehension (e.g. recognising factual information, integrating different textual components, and open-ended responses of concepts from memory) after reading and after a week-long delay. Results indicated that the Sadness (vs. Happiness) condition had higher comprehension scores, with the largest effects emerging for assessments targeting deeper levels comprehension immediately after reading. Eye-tracking analyses revealed that such benefits may be partly driven by sustained attentional focus over the 20-minute reading session. We discuss results with respect to theories on affect, cognition, and text comprehension.
Deep learning has been proved effective in multiple object tracking, which confronts the difficulties of frequent occlusions, confusing appearance, in-and-out objects, and lack of enough labelled ...data. Recently, deep learning based multi-object tracking methods make a rapid progress from representation learning to network modelling due to the development of deep learning theory and benchmark setup. In this study, the authors summarise and analyse deep learning based multi-object tracking methods which are top-ranked in the public benchmark test. First, they investigate functionality of deep networks in these methods, and classify the methods into three categories as description enhancement using deep features, deep network embedding, and end-to-end deep network construction. Second, they review deep network structures in these methods, and detail the usage and training of these networks for multi-object tracking problem. Through experimental comparison of tracking results in the benchmarks in total and by group, they finally show the effectiveness of deep networks for tracking employed in different manners, and compare the advantages of these networks and their robustness under different tracking conditions. Moreover, they analyse the limitations of current methods, and draw some useful conclusions to facilitate the exploration of new directions for multi-object tracking.
In visual tracking, unreliable samples always exist because of occlusion, illumination variation, motion blur, etc. Existing studies have effectively improved the performance of trackers by enhancing ...the quality of online samples. However, an underappreciated view is that not all samples are equally essential to model training. In this paper, we propose a Sample-Aware Adaptive Updating (SAAU) strategy which can actively adjust the update formula by sensing the reliability of samples. Specifically, the Sample-Reliability Awareness (SRA) module can quantify sample reliability by calculating three specific indicators, where the Residual Peak-to-Correlation Energy (RPCE) is designed to cooperate with the other two introduced indicators to obtain credit scores on each sample. Besides, the Self-Guided Update (SGU) module provides a tracker with an unfixed learning rate that matches with the reliability label during updating, where our label annotator generates the label. Extensive experiments on several public benchmarks demonstrate the outstanding compatibility of SAAU and the superiority of our tracker (SAAU-CF) over state-of-the-art approaches.
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