A fall detection method based on a joint motion map using double convolutional neural networks
Automatic fall detection approaches are essential for elderly people, particularly for those who live alone, because of the pressing need for immediate medical assistance. In this paper, we proposed a highly effective fall detection method based ...
A novel underwater sonar image enhancement algorithm based on approximation spaces of random sets
Underwater environment is complex and random. The images obtained from underwater by sonar always have uneven background gray distribution and fuzzy details of boundary. Hence the low-quality sonar images need to be enhanced before analysis. This ...
Abnormal driving behavior detection based on kernelization-sparse representation in video surveillance
The detection of abnormal driving behaviors based on video surveillance systems is an important part of Intelligent Transportation System (ITS), which can help reduce disturbances on traffic flow and improve traffic safety. First, the study ...
A method of vehicle-infrastructure cooperative perception based vehicle state information fusion using improved kalman filter
For the purpose of overcoming the technical bottlenecks and limitations of autonomous vehicles on the information perception, and improving the sensing range and performance of vehicle driving environment and traffic information, a framework of ...
An evaluation of deep neural network models for music classification using spectrograms
Deep Neural Network (DNN) models have lately received considerable attention for that the network structure can extract deep features to improve classification accuracy and achieve excellent results in the field of image. However, due to the ...
Self-attention mechanism in person re-identification models
In recent years, person re-identification based on video has become a hot topic in the field of person re-identification. The self-attention mechanism can improve the ability of deep neural networks in computer vision tasks such as image ...
Detection of passenger flow on and off buses based on video images and YOLO algorithm
Bus passenger flow information is very important as a reference data for bus company line optimization, schedule scheduling basis, and passenger travel mode arrangement. With the development of image processing technology, it has become a current ...
Performance analysis of inverting optical properties based on quasi-analytical algorithms
The inherent optical parameters play a very important role in determining the concentration of seawater components. One such inherent parameter, the absorption coefficient (a), is greatly significant when calculating each component’s content in ...
AMTSet: a benchmark for abrupt motion tracking
Since the OTB100 benchmark dataset is released, it has been widely used in a large number of researches on object tracking for performance evaluation. However, the existing datasets are insufficient to evaluate trackers in handling different ...
Information visualization analysis based on historical data
Visual expression is increasingly used in historical research due to its intuitiveness and distinctness. However, most of the common research contents focus on the spatial concept, but lack the visualization analysis of the attribute ...
Detection of crowdedness in bus compartments based on ResNet algorithm and video images
The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding accurately is a difficult problem. In this paper, firstly, an image sample library is ...
An improved lossless image compression algorithm based on Huffman coding
There is an increasing number of image data produced in our life nowadays, which creates a big challenge to store and transmit them. For some fields requiring high fidelity, the lossless image compression becomes significant, because it can reduce ...
Lightweight multi-scale aggregated residual attention networks for image super-resolution
Recently, single image super-resolution (SISR) based on convolutional neural networks (CNNs) has represented great progress. However, due to the huge number of parameters, these models cannot work well in many real-world applications, most of ...
Two-stream adaptive-attentional subgraph convolution networks for skeleton-based action recognition
Recently, skeleton-based action recognition has modeled the human skeleton as a graph convolution network (GCN), and has achieved remarkable results. However, most of the methods convolute directly on the whole graph, neglecting that the human ...
A coarse to fine framework for recognizing and locating multiple diatoms with highly complex backgrounds in forensic investigation
- Jiehang Deng,
- Haomin Wei,
- Dongdong He,
- Guosheng Gu,
- Xiaodong Kang,
- Hongjin Liang,
- Chao Liu,
- Peijie Wu,
- Yuanli Zhong,
- Shihe Xu,
- Wing-Kuen Ling,
- Jian Zhao
In the forensic investigation, recognizing and locating the multiple diatom objects in an image is a challenging issue due to the interferences of the highly complex backgrounds. To address this issue, a coarse to fine diatom recognition and a ...
Wide receptive field networks for single image super-resolution
Recently, using deep learning(DL) in super-resolution(SR) has ac- hieved great success. These methods combine the convolutional neural network(CNN) to learn a general matrix function for an end-to-end mapping. However, as the width and depth of ...
Continuous physical activity recognition for intelligent labour monitoring
The paper addresses the problem of human activity recognition based on the data from wearable sensors. Human activity recognition depends on a wide context of actions. Activities can not be recognised from the local shape of sensor signals only. ...
Speech emotion recognition based on multi‐feature and multi‐lingual fusion
A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused bylack of large speech dataset and low robustness of acoustic features in the recognition of ...
A Multimodal Approach for Multiple-Relation Extraction in Videos
Automatically interpreting social relations, e.g., friendship, kinship, etc., from visual scenes has huge potential application value in areas such as knowledge graphs construction, person behavior and emotion analysis, entertainment ecology, etc. ...
Underwater image restoration based on exponentiated mean local variance and extrinsic prior
Due to the absorption and scattering of light when it travels in water, underwater imaging has various problems, such as color distortion and low contrast. In general, it is difficult to accurately estimate the transmission map of underwater image ...
Subjective low-light image enhancement based on a foreground saliency map model
Most existing low-light image enhancement methods enhance whole low-light image indiscriminately with the neglect of its subjective content, which may lead to over-enhancement and noise amplification problems in background. In this paper, we ...
Few-shot learning for skin lesion image classification
The mortality of skin pigmented malignant lesions is very high, especially melanoma. Due to the limitation of marking means, the large-scale annotation data of skin lesions are generally more difficult to obtain. When the deep learning model is ...
A curvelet-based multi-sensor image denoising for KLT-based image fusion
The transform-based multi-sensor image denoising methods are inefficient in restoring fine details and texture information of noisy images. The fixed and non-adaptive curvelet transform (CT) design limits its performance in image denoising tasks. ...
End-to-end music emotion variation detection using iteratively reconstructed deep features
Automatic music emotion recognition (MER) has received increased attention in areas of music information retrieval and user interface development. Music emotion variation detection (or dynamic MER) captures also temporal changes of emotion, and ...
A nonlinear prediction model for Chinese speech signal based on RBF neural network
A novel method for Chinese speech time series prediction model is proposed. In order to reconstruct the phase space of Chinese speech signal, the delay time and embedding dimension are calculated by C–C method and false nearest neighbor algorithm. ...
LGVTON: a landmark guided approach for model to person virtual try-on
In this paper, we propose a Landmark Guided Virtual Try-On (LGVTON) method for clothes, which aims to solve the problem of clothing trials on e-commerce websites. Given the images of two people: a person and a model, it generates a rendition of ...
Multi-representation knowledge distillation for audio classification
Audio classification aims to discriminate between different audio signal types, and it has received intensive attention due to its wide applications. In deep learning-based audio classification methods, researchers usually transform the raw signal ...
Fall detection approach based on combined displacement of spatial features for intelligent indoor surveillance
Automatic human fall detection plays a significant role in monitoring senior citizens. Detecting fall events in an intelligent indoor condition can be used as a medium to reduce the consequences of older people being alone. In recent researches, ...
Machine learning model for mapping of music mood and human emotion based on physiological signals
Emotion is considered a physiological state that appears whenever a transformation is observed by an individual in their environment or body. While studying the literature, it has been observed that combining the electrical activity of the brain, ...