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Behavior or human action recognition is one hot research topic in real-time video surveillance system. Dangerous accidents consist of dangerous actions by one or more persons. Thus, action recognition is very important for dangerous accident recognition. If videos captured by public cameras especially dangerous actions related videos can be processed and analyzed immediately to provide an early and...
The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. A key issue is that the neural network training problem is nonconvex, hence optimization algorithms may not return a global minima. This paper provides sufficient conditions...
Adaptive beamforming plays an important role in smart antenna systems. Nonblind adaptive algorithm utilizes the known training sequence to update the weights of antenna array at the cost of low transmission efficiency. Without sending training sequences, blind adaptive algorithm is a solution to implement beamforming and increase the transmission efficiency. Two classic blind algorithms are studied,...
Novelty detection, which aims to determine whether a given data belongs to any category of training data or not, is considered to be an important and challenging problem in areas of Pattern Recognition, Machine Learning, etc. Recently, kernel null space method (KNDA) was reported to have state-of-the-art performance in novelty detection. However, KNDA is hard to scale up because of its high computational...
This paper designs a vibration controller based on back propagation neural network (BPNN) for jacket-type offshore platforms with time delay. The offshore structure is simplified into a single-degree-of-freedom (SDOF) system and transformed into a non-delay model. With an system designed to describe wave forces in the context of Morison equation and wave theory, the original vibration control is formulated...
In order to improve booking tickets experience of the users of Railway Online Ticketing System and ensure the system normally running, Railway Online Ticketing System's users abnormality booking the tickets detection model based on the traditional K-Means and FP-Growth algorithm is proposed. Firstly, preliminary filter user features by the Random Forest Algorithm based on Spark MLlib to identify the...
Due to the finite restricted ground TT&C resources, more and more conflicts appear caused by the increasing TT&C requirements of multi-satellite. The priority determination of TT&C arcs is affected by many factors. It is an important criterion of conflicts elimination for resources scheduling. This paper analyzes the main factors in practice, and put forward a method of using artificial...
Vision-based traffic light detection has been widely studied over the past decade. However, it is still a challenging task to build a real-time and robust classifier-based detector without a high dependency on prior knowledge. In this paper, we have a deep look at the design of features and detection mechanism in the domain of traffic light detection; propose a multi-scale and multi-phase detector...
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability...
Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation...
Scene text detection has attracted great attention these years. Text potentially exist in a wide variety of images or videos and play an important role in understanding the scene. In this paper, we present a novel text detection algorithm which is composed of two cascaded steps: (1) a multi-scale fully convolutional neural network (FCN) is proposed to extract text block regions, (2) a novel instance...
Endmember extraction is a fundamental task in spectral unmixing of remotely sensed hyperspectral images. In this work, we develop a new robust algorithm for endmember extraction which is based on a nonnegative sparse autoencoder. The proposed approach is based on two main steps. First, it uses an automatic sampler approach with local outlier factor and affinity propagation to intelligently gather...
Graph embedding, as a dimensionality reduction framework, has already drawn great attention in hyperspectral image analysis. Taking locality preserving projection (LPP) as example, LPP utilizes typical Euclidean distance in heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with...
This paper developed an approach to determine optimal parameters, C and s, for support vector domain description (SVDD) model to map specific land cover from integrating of training and window-based validation sets (WVS-SVDD). The validation set based on window-based approach made a tighten hypersphere because of compact constraint by the outlier pixels which were located closely to the target class...
An overview of the wind speed retrieval algorithm used to generate the first CYGNSS Level 2 wind speed products is presented. The algorithm uses two observables derived from Level 1b calibrated Delay/Doppler Maps, and constructs a geophysical model function which maps the observable value and its associated incidence angle into a wind speed value. The wind estimates from the two observables are also...
This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based...
Accelerating the inference of a trained DNN is a well studied subject. In this paper we switch the focus to the training of DNNs. The training phase is compute intensive, demands complicated data communication, and contains multiple levels of data dependencies and parallelism. This paper presents an algorithm/architecture space exploration of efficient accelerators to achieve better network convergence...
Content-based image retrieval technology is one of the most important research directions in modern image retrieval technology. With the development of deep learning, the effective features of image can be extracted by well-trained convolution neural networks (CNNs). Based on the extracted image features, we can measure the similarity between two images. Directly comparing image similarity on large...
Echo state network (ESN) is a powerful tool for nonlinear system modeling. However, the random setting of structure (mainly the reservoir) may degrade its estimation accuracy. To create the optimal reservoir for a given task, a novel ESN design method based on differential evolution algorithm is proposed. Firstly, the weight matrix of reservoir is constructed via the singular value decomposition (SVD)...
Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. In this paper, we propose a unified Convolutional Neural Network (CNN) model for this task. In order to reliably detect modern steganographic algorithms, we design the proposed model from two aspects. For the first, different from existing CNN based steganalytic algorithms that use a predefined highpass...
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