People detection is an important task in video surveillance. Due to the people’s similar characteristics and occlusion, crowded people detection for occluded classroom surveillance scenes is challenging. In this paper, a new detection framework based on the relation model method is proposed to detect crowded people in occluded classroom surveillance scenes. Our method is mainly to predict a box set of related objects and then use the positive boxes to refine the noisy boxes. Specifically, a new box set selector is designed to select positive boxes prone to generating accurate predictions, and then the rest occluded boxes are refined through the relation model module. To demonstrate the effectiveness of our proposed method, a new classroom video surveillance dataset ICDU is made, and we conduct extensive experiments on this classroom video surveillance dataset and the public dataset CrowdHuman. Experiment results show that our proposed method performs excellently on our ICDU dataset and CrowdHuman dataset.
Panoptic segmentation is an important method for UAV platforms to implement road condition monitoring and urban planning. In recent years, the panoptic segmentation technology provides more comprehensive information than the current semantic segmentation technology. In this paper, the framework of the panoptic segmentation algorithm is designed for the UAV application scenario. Due to the large target scene and small target of UAV, resulting in the lack of foreground targets in the segmentation results and the poor quality of the segmentation mask. To solve these problems, this paper introduces deformable convolution in the feature extraction network to improve the ability of network feature extraction. In addition, the MaskIoU module is introduced in the instance segmentation branch to improve the overall quality of the foreground target mask. In this paper, a series of data are collected by UAV and organized into UAV_OUC panoptic segmentation dataset. We tested on the UAV_OUC panoptic segmentation dataset. The experimental results on UAV_OUC panoptic benchmark validate the effectiveness of our proposed method.
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