Multi-object tracking (MOT) is a topic of great interest in the field of computer vision, which is essential in smart behavior-analysis systems for healthcare, such as human-flow monitoring, crime analysis, and behavior warnings. Most MOT methods achieve stability by combining object-detection and re-identification networks. However, MOT requires high efficiency and accuracy in complex environments with occlusions and interference. This often increases the algorithm’s complexity, affects the speed of tracking calculations, and reduces real-time performance. In this paper, we present an improved MOT method combining an attention mechanism and occlusion sensing as a solution. A convolutional block attention module (CBAM) calculates the weights of space and channel attention from the feature map. The attention weights are used to fuse the feature maps to extract adaptively robust object representations. An occlusion-sensing module detects an object’s occlusion, and the appearance characteristics of an occluded object are not updated. This can enhance the model’s ability to extract object features and improve appearance feature pollution caused by the short-term occlusion of an object. Experiments on public datasets demonstrate the competitive performance of the proposed method compared with the state-of-the-art MOT methods. The experimental results show that our method has powerful data association capability, e.g., 73.2% MOTA and 73.9% IDF1 on the MOT17 dataset.
The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs the K-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.
that, we train the neural network's sample set, and add the momentum item to correct the weight, so that the neural network can be predicted more quickly and accurately. The main idea of this paper is to predict the future data based on the historical data which are collected by sensor nodes, so as to achieve the purpose of reducing the amount of data transmission in the network and saving the energy of nodes.Finally, the experimental results show that the improved particle swarm optimization algorithm based on weight improved particle swarm optimization neural network algorithm has higher accuracy than the multiple regression method and the grey prediction method. In addition, the method can be used to effectively save energy in wireless sensor data transmission.
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