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Most airport surface surveillance systems focus on monitoring and commanding cooperative objects (vehicles) while neglecting the location and detection of non-cooperative objects (humans). Abnormal behavior by non-cooperative objects poses a potential threat to airport security. This study collects surveillance video data from civil aviation airports in several regions of China, and a non-cooperative abnormal behavior localization and detection framework (NC-ABLD) is established. As the focus of this paper, the proposed framework seamlessly integrates a multi-scale non-cooperative object localization module, a human keypoint detection module, and a behavioral classification module. The framework uses a serial structure, with multiple modules working in concert to achieve precise position, human keypoints, and behavioral classification of non-cooperative objects in the airport field. In addition, since there is no publicly available rich dataset of airport aprons, we propose a dataset called IIAR-30, which consists of 1736 images of airport surfaces and 506 video clips in six frequently occurring behavioral categories. The results of experiments conducted on the IIAR-30 dataset show that the framework performs well compared to mainstream behavior recognition methods and achieves fine-grained localization and refined class detection of typical non-cooperative human abnormal behavior on airport apron surfaces. [ABSTRACT FROM AUTHOR]

Normal behavior is usually more common than abnormal behavior, leading to the problem of category imbalance in the dataset. This can lead to models being biased towards normal behaviors that are easier to identify and less accurate in identifying abnormal behaviors. Meanwhile, the number of people may change in different video frames, and such changes in the number of people can lead to a decrease in the adaptability of most models. To improve the accuracy of abnormal behavior recognition in multi-person scenarios, this paper proposes an intelligent recognition framework based on skeleton pose estimation and person detection, which integrates a pose estimation module, You Only Look Once Human Pose (YH-Pose), and a behavior classification module, Bidirectional Recognition Long Short Term Memory (BR-Lstm). Firstly, the YH-Pose module predicts the 2D positions of human skeletal joint points in the video frames. Secondly, the BR-Lstm module takes as input the skeleton coordinates detached encoding to generate behavioral feature vectors and global feature representations. Finally, a classifier classifies the behavior into normal and abnormal. The proposed framework was experimented on two publicly available datasets: HMDB51 and NTU_RGB+D. The experimental results show that the accuracy of behavioral recognition is 51.3% and 96.7%, respectively, which is better than the mainstream model. The proposed framework was also evaluated with actual surveillance video data. The experimental results showed that the framework could detect five abnormal behaviours on traffic roads: fall , kicking, punching, vomiting and looking down at the mobile phone. The code is avaliable at https://github.com/3083156185/MP-Abr.git. [ABSTRACT FROM AUTHOR]

Human pose estimation in continuous video frames captured from complex coal mine scenes is challenging. The video frames in this scene may suffer from problems such as uneven brightness, blurred image details, and excessive noise. Mainstream pose estimation methods have good detection performance for high-quality static images, but their accuracy and prediction rate can be significantly reduced in coal mine scenes. In this work, a yielding human pose estimation framework is proposed, termed YH-Pose. The framework strives to incorporate additional visual evidences from neighboring frames to facilitate the pose estimation of the current frame. Firstly, a human detector is introduced to locate the person's position in the video frame and extract global features. This can provide a good initialization for the latter keypoint detection, making the training process converge quickly. Secondly, heatmaps are used to encode the joint locations as Gaussian peaks, and a temporal road module (TRM) is designed, which encodes video frames at intervals. The module efficiently fuses spatio-temporal information through frame-rate groups in a hierarchical manner. Lastly, the spatial road module (SRM) learns from fused keypoint context features and predicts the location of the keypoints in the next frame. In addition, a dataset called Colliery-1 is proposed, which derives from underground surveillance video from chinese coal mines and consists of 3600 video clips. The experimental results on the Colliery-1 dataset indicate that the framework achieved an average accuracy of 82% and 80% on the training and test sets, respectively. Moreover, the framework achieved a 94.2% prediction rate for pose estimation. To further evaluate the effectiveness of the proposed method, some comparisons have been made between it and various mainstream methods using different metrics. • Key points are coded in the form of heatmap. • Grouped spatio-temporal feature fusion helps to improve the accuracy. • DCNv2 can improve the performance of locating key points. • The modular design makes it easier to train and optimize the network. [ABSTRACT FROM AUTHOR]