Major countries in the world are facing the problem of aging. Whether it is an internal cause or an external cause of the fall, if the rescue is not timely, it will cause great harm to the elderly. Therefore, we urgently need a real-time and accurate fall detection technology for timely rescue after the elderly fall. For fall detection, the existing sensor-based wearable fall detection devices are expensive to popularize, and there is a problem that the elderly forget to wear them. Therefore, a fall detection model based on AlphaPose combined with LSTM and Lightgbm is proposed. In the algorithm, AlphaPose is first used to extract the key points of the human body, and then two LSTM sub-networks are used to extract temporal and spatial features, and then sent to the main LSTM network for feature fusion. Lightgbm performs classification to achieve more accurate detection results. Experiments were conducted on two fall datasets, KFALL and UR, and the fall detection accuracy rates were 94.43% and 93.81%, respectively.
Aiming at the problems of low detection accuracy and slow detection speed of existing helmet detection models in complex environments, an improved YOLOv8n helmet wearing detection algorithm is proposed in this paper. Firstly, CBAM attention mechanism is added to the backbone network to strengthen the feature extraction capability of the backbone network. Then SimSPPF module is used to replace SPPF module in backbone network to improve the speed of model detection. Finally, DIoU-NMS is used instead of NMS to enhance the detection of occluded targets. The experimental results show that the average detection accuracy of the improved YOLOv8n algorithm is 94.85% and the detection speed is 109.11 FPS, which is 1.41% higher than that of the improved YOLOv8n algorithm.
Large-scale image categorization is a challenging task. In this paper, we propose a new image categorization approach based on visual saliency and bag-of-words model. Firstly, a saliency map is generated by visual saliency method that exploits some coarsely localized information, i.e. the salient region shape and contour. Secondly, size of salient region is acquired by calculating maximum entropy. Thirdly, the local image descriptor-SIFT extracted in the salient region and visual saliency information are combined to build visual words. Finally, the visual word bag is categorized by Support Vector Machine. By comparing with BOW model categorization methods, experiment results show that our methods can effectively improve the accuracy of image categorization.
A fusion algorithm of infrared and visible images based on visual saliency map (VSM) and nonsubsampled contourlet transform (NSCT) was proposed. Usually, the visual salient region of infrared image is directed towards the targets which interpret the most important information in the image. For the given registered infrared and visible images, firstly, the frequency-tuned (FT) saliency detection algorithm is used to calculate the visual saliency map of infrared and visible images. Then the size of each salient region is obtained by maximizing entropy. In order to capture the details of the infrared and visible images, the low and high frequency fusion coefficients of nonsubsampled contourlet transform (NSCT) are selected based on region saliency, region energy (RE) and region sharpness (RS). Four different data sets from TNO, Human Factors are employed, and experimental results indicate that the proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.
As the important technology of remote sensing, surface target detection aims to obtain the information of the surface target, such as water, construction, vegetation and other interesting targets, through the remote sensing image processing and analysis. However, the pre-collection samples of some targets from the single source images are too few to meet the needs of automatic detection in the multi-scale remote sensing images, so target detection is still a challenge. Focus on the problem, a novel target detection method based on transfer learning using multiple sources for surface target in the remote sensing images is proposed. The most remarkable characteristic of transfer learning is that it can employ the knowledge in relative domains to help perform the learning tasks in the domain of the target. With the use of different sources of knowledge, transfer learning can transfer and share the information between similar domains. The proposed method locates the surface target area firstly, and then makes the target samples from different sources involved in learning. Therefore, the similar knowledge conductive to the target can be obtained. The prior knowledge from the multiple sources is transferred to the new target images for target detection. The experimental results show that effect of surface target detection by the proposed method from multiple sources is better than that from the single source, and the accuracy of detection has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the advantage of our method in the multiple sources.
A fusion algorithm of infrared and visible images based on saliency scale-space in the frequency domain was proposed. Focus of human attention is directed towards the salient targets which interpret the most important information in the image. For the given registered infrared and visible images, firstly, visual features are extracted to obtain the input hypercomplex matrix. Secondly, the Hypercomplex Fourier Transform (HFT) is used to obtain the salient regions of the infrared and visible images respectively, the convolution of the input hypercomplex matrix amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale which is equivalent to an image saliency detector are done. The saliency maps are obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. Thirdly, the salient regions are fused with the adoptive weighting fusion rules, and the nonsalient regions are fused with the rule based on region energy (RE) and region sharpness (RS), then the fused image is obtained. Experimental results show that the presented algorithm can hold high spectrum information of the visual image, and effectively get the thermal targets information at different scales of the infrared image.
Automatic target detection in infrared images is a hot research field of national defense technology. We propose a new saliency-based infrared target detection model in this paper, which is based on the fact that human focus of attention is directed towards the relevant target to interpret the most promising information. For a given image, the convolution of the image log amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector in the frequency domain. At the same time, orientation and shape features extracted are combined into a saliency map in the spatial domain. Our proposed model decides salient targets based on a final saliency map, which is generated by integration of the saliency maps in the frequency and spatial domain. At last, the size of each salient target is obtained by maximizing entropy of the final saliency map. Experimental results show that the proposed model can highlight both small and large salient regions in infrared image, as well as inhibit repeated distractors in cluttered image. In addition, its detecting efficiency has improved significantly.
Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use
of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved
that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional
machine learning is that the training and test data should be in the same feature space, and have the same underlying
distribution. If the distributions and features are different between training and future data, the model performance often
drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and
test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our
algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same
underlying distribution by automatically learning a mapping between two different but somewhat similar face images.
According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly
improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and
robustness of our method.
By means of Artificial Neural Network and Back-Propagation algorithm, the multi-component of azo-dyes can be
qualitatively and quantitatively analyzed simultaneously, though their Raman spectra are overlapped. This article
designed a Back-Propagation algorithm network to analyze the multi-component of azo-dyes (Sudan I and Sudan III). In
conclusion, by using the Artificial Neural Network and Raman spectrum can be a good choice for resolving
multi-component.
Being an industrial dye, the Sudan I may have a toxic effect after oral intake on the body, and has recently been shown to cause cancer in rats, mice and rabbits. Because China and some other countries have detected the Sudan I in samples of the hot chilli powder and the chilli products, it is necessary to study the characteristics of this dye. As one kind of molecule scattering spectroscopy, Raman spectroscopy is characterized by the frequency excursion caused by interactions of molecules and photons. The frequency excursion reflects the margin between certain two vibrational or rotational energy states, and shows the information of the molecule. Because Raman spectroscopy can provides quick, easy, reproducible, and non-destructive analysis, both qualitative and quantitative, with no sample preparation required, Raman spectroscopy has been a particularly promising technique for analyzing the characteristics and structures of molecules, especially organic ones. Now, it has a broad application in biological, chemical, environmental and industrial applications. This paper firstly introduces Sudan I dye and the Raman spectroscopy technology, and then describes its application to the Sudan I. Secondly, the fingerprint spectra of the Sudan I are respectively assigned and analyzed in detail. Finally, the conclusion that the Raman spectroscopy technology is a powerful tool to determine the Sudan I is drawn.
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