This paper is concerned with the scaled formation control problem for multi-agent systems (MASs) over fixed and switching topologies. First, a modified resilient dynamic event-triggered (DET) mechanism involving an auxiliary dynamic variable (ADV) based on sampled data is proposed. In the proposed DET mechanism, a random variable obeying the Bernoulli distribution is introduced to express the idle and busy situations of communication networks. Meanwhile, the operation of absolute value is introduced into the triggering condition to effectively reduce the formation error. Second, a scaled formation control protocol with the proposed resilient DET mechanism is designed over fixed and switching topologies. The scaled formation error system is modeled as a time-varying delay system. Then, several sufficient stability criteria are derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). A co-design algorithm based on the sparrow search algorithm (SSA) is presented to design the control gains and triggering parameters jointly. Finally, numerical simulations of multiple unmanned aerial vehicles (UAVs) are presented to validate the designed control method.

Learning and extracting high-level features from point cloud is the key to improving the segmentation performances on point clouds for many networks. At present, many networks present very deep structures to extract high-level features for 3D perception. However, we argue that even better results can be achieved by (i) building feature vectors that integrates multi-scale geometric features, and (ii) exerting discriminative constraints on the learning of mid-levels features. In this paper, we propose a Multi-scale Neighborhood Feature Extraction and Aggregation Model (MNFEAM) to enhance feature extraction for point cloud learning. We try to first extract multi-scale neighborhood information for each input point and then aggregate local information of a mid-level locality feature space, and finally integrate the aggregated local and the global feature vectors. A new discriminative loss function is designed to strengthen the coarse semantics on mid-levels features so that the semantic abstraction process can be improved and accelerated. We improve the performances of three popular networks for point cloud segmentation using the proposed MNFEAM on standard 3D datasets.

Change detection has received extensive attention because of its realistic significance and broad application fields. However, none of the existing change detection algorithms can handle all scenarios and tasks so far. Different from the most of contributions from the research community in recent years, this paper does not work on designing new change detection algorithms. We, instead, solve the problem from another perspective by enhancing the raw detection results after change detection. As a result, the proposed method is applicable to various kinds of change detection methods, and regardless of how the results are detected. In this paper, we propose Fast Spatiotemporal Tree Filter (FSTF), a purely unsupervised detection method, to enhance coarse binary detection masks obtained by different kinds of change detection methods. In detail, the proposed FSTF has adopted a volumetric structure to effectively synthesize spatiotemporal information of the same target from the current time and history frames to enhance detection. The computational complexity analyzed in the view of graph theory also show that the fast realization of FSTF is a linear time algorithm, which is capable of handling efficient on-line detection tasks. Finally, comprehensive experiments based on qualitative and quantitative analysis verify that FSTF-based change detection enhancement is superior to several other state-of-the-art methods including fully connected Conditional Random Field (CRF), joint bilateral filter, and guided filter. It is illustrated that FSTF is versatile enough to also improve saliency detection as well as semantic image segmentation.

This paper presents an indirect adaptive fuzzy control scheme for a class of MIMO non-affine nonlinear systems with unknown dynamics and actuator saturation for greenhouse climate control problems. The objective is to implement output tracking control on nonlinear systems. Using feedback linearization, control inputs with known control gains are first synthesized by well-modeled dynamics of the system, and Taylor series expansion is used to transform unknown non-affine dynamics into the corresponding affine forms. Fuzzy logic systems (FLS) are introduced to estimate the unknown nonlinearity of the transformed affine system and the saturation nonlinearity due to the actuator constraint. The control inputs corresponding to nonlinearity are constructed based on the estimations. By introducing a robust control term, estimation errors and external disturbances are well handled, so as to guarantee the stability when tracking the control process. The control gain estimation obtained by FLS is modified to avoid singularity. Lyapunov stability analysis is performed to derive the adaptive law. To validate the effectiveness of the proposed control scheme, we apply it to a greenhouse climate control problem. The ventilation rate in the greenhouse model is unknown; therefore, it is estimated by FLS. The simulation exhibits satisfactory results, in which the temperature and humidity inside the greenhouse are well tracked.

This paper presents a foreground detection algorithm that is robust against illumination changes and noise, and provides a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online expectation-maximization algorithm that is developed from a basic batch version to update Gaussian mixture models in real time. Then, a spherical K-means clustering method is combined to provide a more accurate direction for the update when illumination is unstable. The combination is supported by the linearity of RGB color reflected from object surfaces, which is both theoretically proved by spectral reflection theory and experimentally validated in several observations. Foreground detection is carried out using a statistical framework with regional judgment. Noise in the detection stage is further reduced by a Bayesian iterative decision-making step. The experiments show that the proposed algorithm outcompetes several classical methods on several datasets, both in detection performance and in robustness to perturbations from illumination changes.

This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online EM algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating, however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.

This paper achieves fast and effective pedestrian detection using Histogram of Oriented Gradient (HOG) descriptor based Support Vector Machine (SVM). A novel approach taking advantage of CodeBook background subtraction(CBBS) is presented in this paper to produce pedestrian samples for SVM. HOG features of the samples are extracted to train Linear and RBF SVM classifiers offline. The classifier is adopted as pedestrian detector in online real-time video sequence detection. The influence of various ratios of positive and negative training sets on detector's performance is carefully investigated. We also compare Linear and RBF SVM in experiments as well. It is concluded that robust feature extraction, proper positive and negative training sample construction, and fine kernel function are crucial for good classification results. Experiments prove that our detector obtains a reliable detection result, which not only satisfies real-time requirement, and is robust against pedestrian appearance and pose variations, illumination changes, background changes, shadows and etc.

This paper presents a background modeling algorithm and a foreground detecting method which is robust against illumination change, providing a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online Expectation Maximization algorithm which is developed from the basic batch edition to update the mixture models in real time. Then a spherical K-means clustering method is used to provide more accurate direction for the update of Gaussian Mixture Models after a deep study of RGB space features under illumination changes. Foreground detection is carried out using a statistical framework and RGB pixel intensity judgments. The results show the proposed algorithm outcompete several classic methods in efficiency, accuracy, and robustness to perturbations from illumination changes, on a sampling of problems.