Abstract Taking photos of sensitive facilities and sensitive information in no photography area may cause sensitive information leakage if not discovered in time. Employing action recognition models to detect instances of photography can effectively prevent information leakage. Current action recognition models have shown unsatisfactory performance in detecting photo-taking actions in surveillance videos, and their reliance on GPU devices hinder their practicality. This paper presents a novel approach to address the detection of photo-taking actions. The method utilizes object detection to filter out background data and incorporates human pose estimation to extract human skeleton data. By combining these AI techniques, the method enables accurate recognition of photo-taking actions. We introduce a novel technique called self-annotation that enables the model to focus on the crucial elements associated with photo-taking actions. Additionally, we introduce a new alarm mechanism that leads to a 69 $$\%$$ % reduction in false positives while maintaining the same level of recall by integrating the labels over a period to recognize actions. Compared with traditional action recognition approaches, our method is more flexible and lightweight in actual engineering applications. Moreover, our model is capable of running on CPU-only devices. Experimental results show that our model achieves a precision of 91 $$\%$$ % on our dataset.

In crowded scenes, detecting pedestrians with high density and various occlusions is always an important yet challenging task. To further improve the performance of one-stage detectors in detecting crowded pedestrians, we propose a one-stage dense pedestrian detection network called YOLO- DensePed (You Only Look Once-Dense Pedestrian Detection) to further overcome shortcoming including limited receptive field, insufficient feature fusion, and ambiguous assignment of anchor boxes for object detection. First, the proposed YOLO-DensePed utilizes a multi-head self-attention module with embedded Gaussian masks to reduce background redundant information, as well as enhance capture capability for global contextual information; Then, Deformable ConvNets v2 (DCNv2) are used instead of standard convolutions in the neck layer, which can dynamically adjust the receptive field and learn the correct feature and multi-scale position information of objects; Furthermore, SimOTA dynamic sample assignment strategy and Soft-NMS post-processing algorithm are also introduced to assist the YOLO-DensePed for better handling occlusion and dense distribution issues. Extensive experiments on the public CrowdHuman dataset demonstrate that YOLO-DensePed consistently presents the best or comparable performance, allowing for efficient and accurate detection of crowded pedestrian.

Object detection is an essential and crucial task in interpretation of optical remote sensing images (RSIs). However, its performance is usually limited due to the complex background and multiscale characteristics of targets. To overcome these limitations, a self-supplementary and revised anchor-free detector is proposed. First, to reduce the computational cost of detection, a partial bottleneck (PBottleneck) structure is designed to efficiently extract multiscale feature information in a lightweight manner. Second, pure spatial feature pyramid network (PSFPN) attaches importance to description of distance and suppresses environmental disturbance by a devised multidirectional distance attention (MDDA) mechanism. In addition, pure fusion strategy (PFS) is created to boost information with no occlusion between various features. Third, toward the multiscale objects issue, self-learning supplementary and revised module (SSRM) is explored to generate more abundant and balanced expression by adaptively incorporating the supplementary and corrected information from adjacent features. Finally, comprehensive experiments are conducted on several publicly available datasets, demonstrating effectiveness of our proposed detector, leading to a new benchmark.

Remote sensing object detection (RSOD) encounters challenges in complex backgrounds and small object detection, which are interconnected and unable to address separately. To this end, we propose an attention-free global multiscale fusion network (AGMF-Net). Initially, we present a spatial bias module (SBM) to obtain long-range dependencies as a part of our proposal global information extraction module (GIEM). GIEM efficiently captures the global information, overcoming challenges posed by complex backgrounds. Moreover, we propose multitask enhanced structure (MES) and multitask feature pretreatment (MFP) to enhance the feature representation of multiscale targets, while eliminating the interference from complex backgrounds. In addition, an efficient context decoupled detector (ECDD) is presented to provide distinct features for regression and classification tasks, aiming to improve the efficiency of RSOD. Extensive experiments demonstrate that our proposed method achieves superior performance compared with the state-of-the-art detectors. Specifically, AGMF-Net obtains the mean average precision (mAP) of 73.2%, 92.03%, 95.21%, and 94.30% on detection in optical remote sensing images (DIOR), high resolution remote sensing detection (HRRSD), Northwestern Polytechnical University Very High Resolution-10 (NWPU VHR-10), and RSOD datasets, respectively.

Object detection is a fundamental task in the analysis and interpretation of remote sensing images. However, compared to natural images, remote sensing images are characterized by broad diversity in object scales, fuzzy objects, and complex background, which bring great challenges to object detection. For overcoming the above problems, a task alignment interaction and cross-scale guidance enhancement network (TCNet) is proposed in this letter. Firstly, a generalized mean spatial pyramid pooling (GeMSPP) is designed and embedded in the backbone to adapt to changes of complex environment and reduce loss of features. Secondly, cross-scale guided enhancement network (CGEN) is proposed to generate high-quality non-aliasing multi-scale target features for each feature level by guiding the fusion of deep features and enhancing feature expression. Thirdly, Task alignment interactive head (TAIH) is adopted to enhance the classification and regression accuracy of the prediction box, so as to suppress background interference and highlight object features. Experiments conducted on public DIOR and RSOD datasets illustrate that the proposed modules can effectively improve the accuracy of detection and our network has superior performance compared with other state-of-the-art detectors.

Object detection is essential in the interpretation of remote sensing images. However, the blurred background and objects with vast variances are identified as the two main challenges of the task. We propose a novel detector adapted to complicated background and multi-scale objects, namely, task-balanced multi-scale adaptive fusion network (TMAFNet), targeting directly on the above two challenges. Firstly, a depth separable global context module (DSGC) is constructed to understand contextual relations among pixels from a global perspective, which is extraordinarily necessary to distinguish objects from the environment. Most importantly, DSGC reduces the computational cost by decoupling the acquisition of global information into single-channel global interaction and multi-channel single-point interaction. Secondly, in order to eliminate disturbance and enhance representation ability of objects, hidden recursive feature pyramid network (HRFPN) is explored, which encodes the information of difference before and after using the multi-scale fusion. HRFPN is proven to enhance the target features by reducing the background noise. Thirdly, a semi-coupling task-balanced head (SCTB) is presented to guarantee the consistency of detection. We have conducted comprehensive experiments on several publicly available datasets, and the results illustrate that our modules improve adaptability and robustness of the network, leading to a new state-of-the-art.

In this paper, we propose a new method of visualizing hierarchical time-series data. We use the hyperbolic tree to visualize the hierarchical structure. The hyperbolic tree can visualize large hierarchical structure. It allocates more space for the nodes of our concern, with the entire hierarchical structure being displayed at the same time. We utilize the time disc, which is similar to the spiral, to display the time-series data. Unlike traditional bar charts and line graphs, the time disc is suited to visualizing large data set and supporting much better the identification of features in the data, such as periodicity and trends. The method can easily display large hierarchical structure and time-series data. We visualize the time series data of each child node by selecting the parent node in the hyperbolic tree, and then observe the similarities and differences between the nodes and the trends of the thing. We applied this method to the urban air quality data visualization and achieved good results.

In this paper, we propose a new method of visualizing hierarchy and time-series data. We use the node-linked technology to show hierarchy structures, rectangles from the left to right to represent the time-series data and a pie chart to represent statistical information about the time-series data. The method is designed to display and compare the corresponding data of each layer, and then observe the differences between the nodes at each layer and the trends of the thing. We applied this method in the urban air quality data visualization and achieved good results.