Abnormal behavior detection by causality analysis and sparse reconstruction

Artificial features are adopted in most of the existing abnormal behavior detection. However, it is difficult to choose and design an effective behavior feature for the reason of highly computational complexity and complex scenarios. To solve this problem, temporal consistency based weak supervised abnormal behavior detection method is proposed in this paper. First, temporal gram matrices are constructed for a given video sequence. Then, a pair of behavior units (Candidate action fragment) are formed by exploiting the temporal consistency and the smoothness of human behavior, which helps to locate the start frame and end frame of the related class of abnormal behavior in the video sequence to train the corresponding classifier. Finally, sparse reconstruction is here utilized to detect abnormal behavior. Experiments conducted on common database including CAVIAR and Crossing demonstrate the effectiveness of the proposed method.

Abnormal behaviour detection is crucial given the reliance on video surveillance for public safety. However, most vision-based methods fail to adapt to the variability and complexity of the crowd scenes inherent in real-world scenarios. This study introduced an abnormal crowd behaviour detection algorithm based on lightweight Graphic Neural Networks (GNNs) using trajectory similarities and spatial-temporal attributes. Integrating Graph Attention Networks (GATs) to encode the graph structure, creating latent embeddings that capture both local and global relationships within crowds. Also introducing an autoencoder (AE) structure to identify anomalies by comparing true movement with the decoded output. Anomaly is detected when the reconstruction error is high. Experiments on UCSD Pedestrians and ShanghaiTech campus datasets show the successful use of trajectory similarities as graph features for GNNs to generate logical embeddings of crowd movement and highlight the impact of time windows on anomaly detection precision as reflected in F1-scores. The Area Under the Curve (AUC) metric shows stability, highlighting the model’s balanced performance. Qualitative analysis of the ShanghaiTech campus dataset illustrates the model’s ability to align detection with visual anomalies. Overall, this study demonstrates the effectiveness of the proposed graph-based anomaly detection framework for crowd monitoring through a lightweight model suitable for CCTV.

As society develops, the demand for security is rapidly increasing. Accordingly, there is growing interest in research on methods to detect and prevent abnormal behavior using surveillance cameras in public places and private spaces such as shopping malls and airports for human safety. Many detection techniques based on deep learning models have been researched in the field of abnormal behavior detection. However, due to the lack of labeled abnormal behavior data, there are significant difficulties in developing an effective detection system. This paper surveys methods for deep learning methods to detect abnormal human behavior in surveillance video and presents recent techniques. First, I will introduce popular datasets that have often been used in previous research. After that, we categorized the existing methods for detecting abnormal behavior using deep learning into three types: supervised learning, unsupervised learning, and partially-supervised learning. We then explained the basic concepts and advantages of each method and summarized their shortcomings. We also briefly describe future research directions based on the advantages and disadvantages of each method. Based on this, it is expected that the technology of video surveillance systems that apply abnormal behavior detection will further develop.

With the development of track and field, people pay more and more attention to the quality of classroom teaching of track and field technology, and the evaluation of teaching quality plays a key role in it. In today's educational reform, teaching evaluation plays an important role as an important method to test teachers' teaching and students' learning. With the rapid development of machine learning, especially deep learning, image-based individual abnormal behavior detection technology is becoming more and more mature, but there are still many difficulties to be solved in video-based group abnormal behavior detection technology. Therefore, it is necessary to study the detection algorithm. Based on machine vision theory, image processing theory and video analysis technology, this paper studies three key technologies involved in human abnormal behavior detection in video: moving target detection, moving target tracking and abnormal behavior detection.

Abnormal crowd behavior detection has recently attracted increasing attention due to its wide applications in computer vision research areas. However, it is still an extremely challenging task due to the great variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents. To tackle these challenges, we propose a new probabilistic framework named variational abnormal behavior detection (VABD), which can detect abnormal crowd behavior in video sequences. We make three major contributions: (1) We develop a new probabilistic latent variable model that combines the strengths of the U-Net and conditional variational auto-encoder, which also are the backbone of our model; (2) We propose a motion loss based on an optical flow network to impose the motion consistency of generated video frames and input video frames; (3) We embed a Wasserstein generative adversarial network at the end of the backbone network to enhance the framework performance. VABD can accurately discriminate abnormal video frames from video sequences. Experimental results on UCSD, CUHK Avenue, IITB-Corridor, and ShanghaiTech datasets show that VABD outperforms the state-of-the-art algorithms on abnormal crowd behavior detection. Without data augmentation, our VABD achieves 72.24% in terms of AUC on IITB-Corridor, which surpasses the state-of-the-art methods by nearly 5%.

This paper proposes a new algorithm for abnormal behavior detection in surveillance video. Firstly, the motion information image of each frame is constructed by calculating the optical flow size and the angle difference between the optical flow vectors between consecutive frames, and then the obtained motion image information is input into the convolutional neural network (CNN) for training, and used for video abnormal behavior detection. In the algorithm, the motion information image generated based on optical flow information can provide the motion information features in the video image more accurately, which makes it easier to distinguish the normal behavior and abnormal behavior of the video. The experiment of this algorithm is carried out on the commonly used data set PETS 2009. Experimental results show that the proposed method and other algorithms have a significant improvement in the accuracy of abnormal behavior detection.

Video abnormal behavior detection is an important research direction in the field of computer vision and has been widely used in surveillance video. This paper proposes an abnormal behavior detection model based on human skeletal structure and recurrent neural network, which uses dynamic skeletal features for abnormal behavior detection. The model in this paper extracts key points from multiple frames through the human pose estimation algorithm, obtains human skeleton information, and inputs it into the established autoencoder recurrent neural network to detect abnormal human behavior from video sequences. Compared with traditional appearance-based models, our method has better anomaly detection performance under multi-scene and multi-view.KeywordsAbnormal behavior detectionGRUAutoencoderSkeletal information

With the rapid development of the Internet, the world is gradually moving toward the information age, and the current cyber security situation is getting more and more serious. Traditional methods based on statistical analysis, feature proximity, tensor decomposition, etc. have detection limitations and low detection accuracy when addressing user network security issues. In this paper, we propose an anomaly behavior detection method based on GCN-BiLSTM. First, a graph convolutional neural network is used as a feature extractor to extract the useful graph structure information in the network as a representation vector for the entire graph. Then, a bidirectional long short-term memory network method with an integrated attention mechanism is used for training, and the abnormal behavior detection is completed by combining the extracted feature information. Experiments were conducted on the IDS2017 dataset and compared with the current typical abnormal behavior detection methods, showing that the accuracy of the GCN-BiLSTM based anomaly detection method is further improved and the overall performance is better, which verifies the effectiveness of the proposed method.

In the age of Internet, information security has received more and more attention, and network abnormal behavior detection technology has emerged as the times require. Aiming at the lack of accuracy and flexibility of traditional abnormal behavior detection technology in detecting multi-class malicious behaviors, this paper proposes a network abnormal behavior detection technology based on gcForest. The compareson through experiments proves that the technology has obvious advantages in the field of network abnormal behavior detection.

Breast cancer is one of the main causes of cancer deaths around the world and is known for its aggressive growth and ability to spread. While machine learning has shown good results for diagnosis, most existing methods do not handle uncertainty or explain their predictions clearly. In this study, we present an integrated framework that combines uncertainty-aware ensemble learning with causal feature analysis and multimodal explainability for breast cancer prediction. The framework uses a mix of Light Gradient Boosting Machine (LightGBM), random forest, and gradient boosting classifiers that include uncertainty estimation so that the model can mark predictions that are less confident. It also applies causal analysis to detect possible clinical confounders and uses SHAP (Shapley Additive Explanations), permutation importance, and feature attribution for interpretation. Tests on two public datasets showed strong and consistent performance. On the UCTH Clinical Dataset, the model reached an area under the curve (AUC) of 0.97%, an accuracy of 0.95%, and an F1 score of 0.94%, with 100% precision for high confidence cases and no false positives. On the Breast Cancer Wisconsin dataset, it achieved an AUC of 0.99, an accuracy of 0.94%, and an F1 score of 0.92%, which increased to 0.98% accuracy and 0.98% F1 score when only certain predictions were considered. Causal analysis pointed out important clinical confounders like lymph node involvement, tumor size, and metastasis, while fairness tests showed balanced results across demographic groups. Overall, the framework combines uncertainty estimation and causal interpretability to give predictions that are both accurate and trustworthy. It provides clinicians with clear confidence levels for every prediction and supports transparent decision-making that can reduce diagnostic errors and improve reliability in clinical use.

Human action recognition from videos is a challenging task in computer vision. In recent years, histogram-based descriptors that are calculated along dense trajectories have shown promising results for human action recognition, but they usually ignore motion information of the tracking points, and the relationship between different motion variables is not well utilized. To address this issue, we propose a motion keypoint trajectory (MKT) approach and a trajectory-based covariance (TBC) descriptor, which is calculated along the motion keypoint trajectories. The proposed MKT approach tracks motion keypoints at multiple spatial scales and employs an optical flow rectification algorithm to reduce the influence of camera motions and thus achieves better performance than the improved dense trajectory (IDT) approach well known in the literature. In particular, MKT is faster than IDT, because MKT does not need to use human detection and extracts fewer trajectories than IDT. Furthermore, the TBC descriptor outperforms the classical histogram-based descriptors, such as the Histogram of Oriented Gradient, Histogram of Optical Flow and Motion Boundary Histogram. Experimental results on three challenging datasets (i.e., Olympic Sports, HMDB51 and UCF50) demonstrate that our approach is able to achieve better recognition performances than a number of state-of-the-art approaches.

Assessing the usability of objects for a specific population can be laborious and time consuming. Furthermore, for the older adult population, the usability of objects involved in the completion of tasks of daily living is critical to ‘aging-in-place’ and the preservation of independence. This paper explores the automation of the process of observing older adults with Alzheimer's as they use various types of faucets. Features extracted from video and audio signals encode the subjects' progression through a hand-washing task and temporal segmentation is used to determine the state of the process at each video frame. Histograms of optical flow, a hand-tracking particle filter, and a water detection algorithm are used to extract features encoding the state of the handwashing process. A Hidden Markov Support Vector Machine is used to label each video frame of the handwashing process as belonging to one of five states with an overall accuracy of 93.58%.

In this paper, we propose a novel approach to extract local descriptors of a video, based on two ideas, one using motion boundary between objects, and, second, the resulting motion boundary trajectories extracted from videos, together with other local descriptors in the neighbourhood of the extracted motion boundary trajectories, histogram of oriented gradients, histogram of optical flow, motion boundary histogram, can be used as local descriptors for video representations. The motion boundary approach captures more information between moving objects which might be caused by camera movements. We compare the performance of the proposed motion boundary trajectory approach with other state-of-the-art approaches, e.g., trajectory based approach, on a number of human action benchmark datasets (YouTube, UCF sports, Olympic Sports, HMDB51, Hollywood2 and UCF50), and found that the proposed approach gives improved recognition results.

On an algorithm for human action recognition

Gait has been known as an effective biometric feature to identify a person at a distance, e.g., in video surveillance applications. Many methods have been proposed for gait recognitions from various different perspectives. It is found that these methods rely on appearance (e.g., shape contour, silhouette)-based analyses, which require preprocessing of foreground–background segmentation (FG/BG). This process not only causes additional time complexity, but also adversely influences performances of gait analyses due to imperfections of existing FG/BG methods. Besides, appearance-based gait recognitions are sensitive to several variations and partial occlusions, e.g., caused by carrying a bag and varying a cloth type. To avoid these limitations, this paper proposes a new framework to construct a new gait feature directly from a raw video. The proposed gait feature extraction process is performed in the spatio-temporal domain. The space-time interest points (STIPs) are detected by considering large variations along both spatial and temporal directions in local spatio-temporal volumes of a raw gait video sequence. Thus, STIPs are allocated, where there are significant movements of human body in both space and time. A histogram of oriented gradients and a histogram of optical flow are computed on a 3D video patch in a neighborhood of each detected STIP, as a STIP descriptor. Then, the bag-of-words model is applied on each set of STIP descriptors to construct a gait feature for representing and recognizing an individual gait. When compared with other existing methods in the literature, it has been shown that the performance of the proposed method is promising for the case of normal walking, and is outstanding for the case of partial occlusion caused by walking with carrying a bag and walking with varying a cloth type.