Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos.

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Due to the complexity and diversity of indoor environment objects and interference occlusions, the accuracy of multi-object target detection based on 3D point cloud is limited. To address this issue, we present a multi-target detection method based on adaptive feature adjustment (AFA) of 3D point cloud. First, our method preprocesses the dataset and constructs a backbone module. Afterwards, our method uses an improved PointNet[Formula: see text] network for feature adaptive learning, where an AFA module is added to learn the influence relationship between point pairs. The proposed method then establishes the relationship between contexts in the local point set area and extracts the feature of point cloud. Using the idea of Hough voting, our method can generate some votes close to the particle. Using these votes to generate proposal, the proposed method adds CBAM attention mechanisms to both modules of voting and proposal, which can fuse the feature information of the channel and expand the receptive field in space. Our method can enhance the important features and weaken the unimportant features, making the extracted features more directional and enhancing the expressiveness of the network. Finally, the generated results are visualized to complete the multi-target detection of 3D point cloud. To verify the effectiveness of our proposed method, two large datasets with real 3D scanning, scanNet2 and SunRGB-D, are used for training the network. The experimental results show that the proposed method can improve the effectiveness of point cloud target detection in indoor scenes, getting a higher detection accuracy.