Pedestrian detection is paramount for advanced driver assistance systems (ADAS) and autonomous driving. As a key technology in computer vision, it also finds many other applications, such as security and surveillance etc. Generally, pedestrian detection is conducted for images in visible spectrum, which are not suitable for night time detection. Infrared (IR) or thermal imaging is often adopted for night time due to its capability of capturing the emitted energy from pedestrians. The detection process firstly extracts candidate pedestrians from the captured IR image. Robust feature descriptors are formulated to represent those candidates. A binary classification of the extract features is then performed with trained classifier models. In this paper, an algorithm for pedestrian detection from IR image is proposed, where an adaptive fuzzy C-means clustering and convolutional neural networks are adopted. The adaptive fuzzy C-means clustering is used to segment the IR images and retrieve the candidate pedestrians. The candidate pedestrians are then pruned using human posture characteristics and the second central moments ellipse. The convolutional neural network is used to simultaneously learn relevant features and perform the binary classification. The performance of the proposed algorithm is compared with state-of-the-art algorithms on publicly available data set. A better detection accuracy with reduced computational accuracy is achieved.

Abstract:Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly detection is the RX detector. A number of variations have been studied since then, including global and local versions for different type of anomalies. Aiming at a real-time requirement for practical applications, this paper extends the concept of global and local anomaly detectors to be real-time detectors. The algorithms exploit the fact that a true real-time detector must produce its output in a causal manner and at the same time as an input comes in. Taking advantage of the Woodbury matrix identity, the global and local real-time detectors can be implemented and processed pixel-by-pixel in real time. Both synthetic and real hyperspectral imagery are conducted to demonstrate their performance.

Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments.

The aim of this study was to evaluate the chitin degradation potential and whole-genome sequence of Streptomyces diastaticus strain CS1801, which had been screened out in our previous work. The results of fermentation revealed that CS1801 can convert the chitin derived from crab shells, colloidal chitin and N-acetylglucosamine to chitooligosaccharide. Additional genome-wide analysis of CS1801 was also performed to explore the genomic basis for chitin degradation. The results showed that CS1801 possesses a chromosome with 5,611,479 bp (73% GC) and a plasmid with 1,388,284 bp (73% GC). The CS1801 genome consists of 7584 protein-coding genes, 90 tRNA and 21 rRNA operons. In addition, the results of genomic CAZyme analysis indicated that CS1801 comprises 103 glycoside hydrolase family genes, which could regulate the glycoside hydrolases that contribute to chitin degradation. The whole-genome information of CS1801 could highlight the mechanism underlying the chitin degradation activity of CS1801, strongly indicating that CS1801 is characterized by a substantial number of genes encoding chitinases and the complete metabolic pathway of chitin, conferring CS1801 with promising potential applicability in chitooligosaccharide production.

In this paper, we first give the definition of asynchronous communication topology of second-order multiagent systems. Then we discuss respectively the leaderless and leader-following consensus problems of the second-order multiagent systems under asynchronous communication topology. The proposed control protocols based on nearest-neighbor interaction rules can guarantee the leaderless and leaderfollowing consensus to be achieved, as long as the fixed position communication graph is undirected and connected, the switching velocity communication digraph is balanced and jointly weakly connected in any two contiguous switching timeinterval. Finally, simulation results are presented to support the effectiveness of our theoretical results.

Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods.

No abstract

Aiming at improving the tracking performance of the delta-generalized labeled multi-Bernoulli (δ-GLMB) filter, we present a one time step lagged δ-GLMB smoother in this work, which also inherently outputs targets trajectories and differs from the Probability hypothesis density (PHD), Multi-Bernoulli (MB), and Cardinalized probability hypothesis density (CPHD) smoothers that are incapable of generating target trajectories directly. Under the standard multitarget measurement likelihood and state transition kernel, we show that a δ-GLMB distributed multitarget filtering density would result in a same distributed one time step lagged multitarget smoothing density. An efficient implementation of the proposed smoothing algorithm using the standard ranked assignment technique is also given. Numerical results show that the proposed smoother is capable of tracking a time-varying number of targets, in the presence of measurement origin uncertainty, target detection uncertainty, and clutter, and show that the proposed smoother outperforms the δ-GLMB filter, and the PHD, MB, and CPHD smoothers of the same time lag on both the estimates of target number and state and it also outperforms the LMB and the approximated δ-GLMB smoothers of the same time lag on target number estimate. INDEX TERMS Delta-generalized labeled multi-Bernoulli, multitarget tracking, random finite set, smoothing.