Weak Supervised Learning Based 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%.

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.

In this paper; we propose a novel crowd behavior representation method to detect abnormal behaviors in videos. An adaptive optical flow filtering method is proposed to utilize low-level optical flow informations. Furthermore, a simple framework is developed to detect and to localize abnormal crowd behavior using adaptive optical flow filtering result. The proposed method is more robust than other modeling methods in representing different behaviors. In this model, a normal behavior is presented by the general value. Some outliers in the temporal domain or spatial domain are presented by a higher value. Spatio-temporal cuboids are extracted from the filtering result to present the likelihood of anomaly in the frame. Experimental evaluations are performed on two public datasets with comparison to the provisos abnormal behavior detection methods in the literature. Experimental results show that the proposed methods outperform previous abnormal behavior detection techniques in the literature.

We propose a method to detect abnormal crowd behavior using integrated multiple behavior models. Traditional abnormal detection methods are based only on personal behavior models. However, a single behavior model cannot accurately reflect complex crowd behavior. To solve this problem, we introduce an integrated multiple behavior model for accurate abnormal behavior detection in a complex crowd scene. We use not only the personal behavior model, but also multiple social behavior models. The experimental results show that our proposed method efficiently detects the abnormal behavior in a crowded scene.

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.

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 order to improve the effect of network public opinion data abnormal behavior detection, an artificial intelligence-based network public opinion data abnormal behavior detection method is proposed. By constructing the network public opinion data model, recognizing the evolution rule of network public opinion data, locating the abnormal data area according to the behavior detection algorithm, and using the probability neural network under artificial intelligence to detect the abnormal data behavior. The experimental results show that the detection method proposed this time is 28.12% and 84.37% higher than the two traditional methods when detecting large-scale public opinion abnormal behavior data. It can be seen that the detection method based on artificial intelligence is not restricted by the volume of network data, and the detection effect is better.KeywordsArtificial intelligenceNetwork public opinionData abnormal behaviorDetection method

The installation of surveillance systems in public or private settings is increasingly integrated with intelligent and real-time video anomaly detection to proactive threat consideration. A framework is proposed for real-time abnormal behavior detection for streaming video data based on Dynamic Graph Neural Networks (DGNNs). Unlike traditional frame-based models, the DGNN is not restricted to a static set of nodes; instead, it treats each frame as a graph, where nodes denote detected physical objects such as people and vehicles. DGNN introduces real-time frame-wise graph updates unlike prior methods with static or slow graph refresh using motion, pose, and interaction cues, supported by a lightweight spatio-temporal GCN optimized for live anomaly detection. The results convey high performance for the model, which recorded an accuracy of 99.65%, along with the corresponding precision, recall, and F1-score. An FNR and FPR of only 0.35% and 0.03%, respectively, highlighted its strong performance in discriminating normal from anomalous behaviors. The ROC and precision-recall AUC scores zoomed close to 1.0000 in almost all categories, thereby attesting to the system’s robustness where even slight stakes are involved. Following this was the fusion of TensorRT optimization in ensuring the real-time processing capability of the model, with an inference latency of fewer than 200 milliseconds per segment. The DGNN system, therefore, represents a scalable, low-latency option that is capable of real-time detection and classification of diverse and complex real-world abnormal behaviors, making it a novel approach toward smart public safety systems with very little dependency on huge annotated datasets.

ABSTRACTThe detection of abnormal behaviours with fast and automatic recognising is significant in crowded areas to provide higher security to the public. The adoption of deep learning and machine learning‐based abnormal behaviour detection models enhances the influential detection and real‐time security monitoring in crowds. The researchers have remotely evaluated the heart rate based on physiological information to detect abnormal activities in various years. Over the past few years, several progress have been made, and there are still some issues concerning processing time, accuracy, and computational complexity. The developed approaches detects the activities of anomalies like traffic rule violations, riots, fighting, and stampede, in addition, several anomalous entities such as abandoned luggage and weapons at the sensitive place automatically in time. However, the identification of video anomalies methods poses several challenges because of various environmental conditions, the ambiguous nature of the anomaly, lack of proper datasets, and the complex nature of human characteristics. In recent days, there have been only a few devoted surveys associated with deep learning related video anomaly identification as the research domain is in its initial stages. In this review work, the abnormal behaviour analysis models using deep learning are reviewed in depth in for security applications. Based on the traditionally used abnormal behaviour analysis models in crowded scenes, we widely categorised the methods into classification using object tracking, classification using handcrafted extracted features, classification using non‐contact heart rate variability and blood pressure, analysing motion patterns from the visual frames, and classification using face images. We also discuss the comparative analysis of the previous methods with respect to datasets, computational infrastructure, and performance measures for both qualitative and quantitative analysis. In addition, the open and trending research challenges are analysed for future research.

A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.

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.

A novel automatic detection method for abnormal behavior of single fish using image fusion

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.

Modeling abnormal behavior detection in the intelligent video monitoring system for recognition or detection of special event has attracted significant research interest in recent years. In order to achieve more effective recognition of abnormal behavior detection of the intelligent video surveillance system, this paper proposes a working human abnormal operation recognition approach based on deep multi-instance sorting model. Firstly, the uncut long video is sparse to obtain normal and abnormal behavior video segments, and the RGB and optical flow features in the segment are extracted by the deep convolution network. Video feature vectors are obtained by the consensus function and feature extractor. Then, multi-instance sorting learning is used to assign abnormality scores between 0 and 1 to feature vectors. When the abnormal values of abnormal packets are higher than those of normal packets, the abnormality score is returned and the high abnormality score is determined as an abnormal behavior. The experiments on the open THUMOS14 data set and our own XAGCWD data set using CUDA GPU accelerated computing to demonstrate that our approach improves the recognition accuracy about 10.8% and high accuracy of abnormal detection. The main purpose of this research is to apply the model proposed in this paper to the intelligent workshop behavior monitoring system to effectively realize the safety management of workshop personnel.