Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.

In recent years, the use of boundary information in saliency detection has been receiving increasing attention. In some cases, existing methods can output saliency maps with clear object boundaries by learning boundary information. However, their boundary prediction structures are generally separated from the prediction branches of the salient regions, and the resulting boundary features may not match the salient objects. We propose a simple saliency detection unit (SDU) to learn more accurate boundary features, and apply multiple such units to construct a boundary information progressive guidance network (BIPGNet). The SDU cascades the salient region and boundary detection, where the boundary features are directly extracted from the salient regions. In the BIPGNet, semantic and boundary features are progressively merged to produce complementary features. We use the complementary features of each stage in one SDU for detecting the salient objects. In addition, a novel boundary information guidance (BIG) module is designed that focuses on the boundary information in a feature layer. We apply multiple BIG modules to the complementary features at different stages. The quality of output saliency map is improved by modifying the complementary features. Experimental results demonstrate that our method can achieve better performance on five benchmark datasets, consistently surpassing 15 state-of-the-art methods. Our source code is publicly available at https://github.com/CKYiu/BIPG.

Fast and robust detection of a small target in an infrared image is one of the key techniques in infrared search and track systems. The top-hat transformation is an important technology in the field of infrared target detection. Many algorithms have been proposed to improve the top-hat transformation by modifying different structural elements. However, these methods can cause a high false alarm rate under conditions of small dim targets, low signal-to-noise, and complex background. To overcome this limitation, considering both detection performance and speed, an effective and robust detection model using multidirectional structural elements is proposed in this article. First, the nonconcentric multidirectional ring structural elements are designed. Then, using the morphological transformation with designed structural elements, the background is suppressed, and a small target is enhanced in different directions. Finally, the results of the morphological transformations are fused to obtain the final result, further highlighting a small target. The experimental results demonstrate the robust performance of the proposed method for various backgrounds and targets. In addition, the proposed method can achieve a detection speed of up to 10.4 ms per frame under the image resolution of 240 $ \times $ 320 pixels. Therefore, the proposed method is suitable for real-time applications.