A working human abnormal operation recognition approach based on the deep multi-instance sorting model

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.

This paper designs an intelligent greenhouse monitoring system to achieve the purpose of intelligent and precise acquisition, storage, and transmission of various data.The main control chip of the system is the STM32F103C8T6 minimum system board, which uses soil moisture sensors, photoresistors, temperature and humidity sensors, raindrop sensors, CO2 sensors, and other sensors to collect data.An OLED display shows the monitored data on site, and an ESP8266 module transmits the data to the ONENET cloud platform via Wi-Fi.Then, an abnormal indoor parameter generates a high-level trigger to activate the buzzer alarm.After physical testing, the intelligent monitoring system can achieve intelligent monitoring of various environmental data such as indoor and outdoor temperature and humidity, light intensity, and air quality.

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%.

An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Power transient identification

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky trap images and an improved Cascade R-CNN

The increased use of surveillance technology and the need for robust security have fueled research into autonomous, intelligent monitoring systems, particularly in shopping centers where theft and vandalism are common. Deep learning, though promising in anomaly detection, is still hampered by the quality and balance of the training data. In this work, we introduce a deep learning-based anomaly detection system that is trained on an optimized dataset where extensive preprocessing was performed to enhance data quality, eliminate redundancies, and balance class distributions.Our DenseNet-121 and CNN classifier-based system uses this optimized dataset to improve abnormal behavior detection in store surveillance. Experimental results confirm that dataset optimization significantly improves anomaly detection accuracy, reduces false positives, and improves generalizability. Our system achieves an 83% total accuracy, outperforming systems trained on raw, imbalanced data. These findings underscore the importance of data preprocessing as an integral part of deep learning-based surveillance systems.

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.

In order to better ensure the effective operation of substation intelligent auxiliary monitoring system, aiming at the problems of slow data transmission speed and poor data processing security of current monitoring system, a high-speed data transmission method of the substation intelligent auxiliary monitoring system is proposed. By constructing the data processing platform of substation intelligent auxiliary monitoring system, the data transmission algorithm of auxiliary monitoring system is optimized, reasonably select the optimal path of data transmission and conduct security encryption processing, so as to improve the high-speed data transmission efficiency of substation intelligent auxiliary monitoring system. Finally, the experiment shows that the high-speed data transmission method of the substation intelligent auxiliary monitoring system has higher practicability in the process of practical application and fully meets the research requirements.

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.

Towards automated greenhouse: A state of the art review on greenhouse monitoring methods and technologies based on internet of things

Creating intelligent information processing systems for monitoring complex technical objects and systems is an important task today. This paper presents the principles and models for creating systems of this class. The decision-making process in intelligent information-measuring monitoring systems is described. The article presents a model of intelligent monitoring system and a model of decision-making in intelligent monitoring systems. The presented model of decision-making in intelligent monitoring systems allows estimating the efficiency of their functioning. The article proposes a structural scheme reflecting the sequence of decision-making tasks.

At the present stage of development of dairy farming, the main task facing the industry is the effective management of all processes that ensure the operation of a farm with a large livestock. This task will not be achievable without the use of digital intelligent production control and management systems, tracking the health of animals and their productivity. There are many systems that integrate into production and allow you to monitor the health of cows on a dairy farm. These systems are based on the identification, tracking and collection of information about the physiological state of animals through various sensors. However, all these systems are expensive, demanding to maintain, often fail due to aggressive operating conditions. The article considers the possibility of using an intelligent video monitoring system to analyze the physiological state of cows and predict its changes. To substantiate the requirements for an intelligent video monitoring system of the physiological state of cows, a mathematical model of the life of productive cows in the form of a graph of states is proposed. This method graphically displays the possible states of the system and their possible transitions from state to state. Having constructed a graph of states, relying on the central probability theorem for event flows, it is possible to determine the probability and intensity of state transitions using differential equations. For the functioning of the video monitoring system for analyzing the physiological state of cows and predicting its changes, the proposed mathematical model can be the basis of the system and be based on the collection and analysis of video information about animal behavior. When covering the entire territory of the animal's living space with video cameras, the intelligent video monitoring system will be able to provide early diagnosis of changes in the physiological state of the observed animals and issue a warning, which will allow for timely therapeutic and preventive measures.

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.

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.

Intelligent battery management system in a smart farm has a very significant role for energy management. It gives the energy demand status of the entire farm, the available energy produced by the PV system, and the energy stored in the batteries. However, it is important that energy monitoring system to be efficient, most especially in optimizing the distribution of the available harvested energy. Also, since the loads will come from various systems such as aquaponics, vision system, irrigation system and communication systems, efficient system is a necessity. Therefore, it is necessary to have an intelligent monitoring system that will acquire all the possible energy demands of each respective system loads. With the aid of smart farming technology, accurate energy monitoring and distribution is made possible. To properly manage the use of energy harvested for smart farm system loads, this study discusses the design of a fuzzy logic-based intelligent battery management monitoring system (FLi-BMS) to optimize the energy distribution to several loads in the smart farm.