Moving Object Detection and Classification Using Neuro-Fuzzy Approach

The intelligent campus surveillance system is beneficial to improve safety in school. Abnormal behavior recognition, a field of action recognition in computer vision, plays an essential role in intelligent surveillance systems. Computer vision has been actively applied to action recognition systems based on Convolutional Neural Networks (CNNs). However, capturing sufficient motion sequence features from videos remains a significant challenge in action recognition. This work explores the challenges of video-based abnormal behavior recognition on campus. In addition, a novel framework is established on long-range temporal video structure modeling and a global sparse uniform sampling strategy that divides a video into three segments of identical durations and uniformly samples each snippet. The proposed method incorporates a consensus of three temporal segment transformers (TST) that globally connects patches and computes self-attention with joint spatiotemporal factorization. The proposed model is developed on the newly created campus abnormal behavior recognition (CABR50) dataset, which contains 50 human abnormal action classes with an average of over 700 clips per class. Experiments show that it is feasible to implement abnormal behavior recognition on campus and that the proposed method is competitive with other peer video recognition in terms of Top-1 and Top-5 recognition accuracy. The results suggest that TST-L+ can improve campus abnormal behavior recognition, corresponding to Top-1 and Top-5 accuracy results of 83.57% and 97.16%, respectively.

This paper presents a VLSI architecture of real-time object detection design for the intelligent video surveillance systems. In order to achieve the targets of high performance, low cost, and high accuracy, an efficient block-based background subtraction (BBS) algorithm had been created for VLSI implementation. It included a background model, a luminance generator, and a block difference model. The warning signals in the intelligent video surveillance systems will be triggered once more than a threshold number of blocks are marked as object discovered blocks. The VLSI architecture of this work contained 4.6-K gate counts and consumed 2.05 mW when it operated at 100 MHz processing rate by using a TSMC 0.18 um CMOS process. Compared with previous low-complexity designs, this work had the benefits of lower cost, higher performance and higher accuracy.

Citizen insecurity in underdeveloped third world countries is aggravated by poor management of arms control and illegal trafficking, which requires information technology solutions in intelligent video surveillance systems for the detection of lethal weapons. The literature review highlights the need for an intelligent video surveillance system to combat high crime, using fog computing, which processes data in real time for the detection of weapons and other crimes. Furthermore, at an international level, solutions based on artificial intelligence and deep learning are being implemented for object recognition and weapons detection. Therefore, this paper describes the design of an intelligent video surveillance system based on artificial vision, fog and edge computing to detect lethal weapons in domestic environments, performing weapon classification and data transmission to police centers. The intelligent video surveillance system allows detecting lethal weapons and operates in three stages: an edge node with a Raspberry Pi 4; a detection algorithm based on a convolutional neural network with YOLOv5; and streaming tagged images to a security unit via WhatsApp. The main conclusion is that the system achieved a precision greater than 0.85 and a quick and efficient response in sending alerts, representing a scalable and effective solution against home burglary.

This paper proposes a new intelligent video surveillance system based on Android phone. By using Android phone, this system can be deployed easily and quickly. Compare with the traditional surveillance system, this new one uses Android phone to replace the large and expensive surveillance cameras. In addition, the functions of back-end equipment of the traditional system have been moved to the front-end equipment. With a modified invasion detection algorithm, the front-end equipment becomes more intelligent. This schema can significantly increase the mobility and flexibility of the entire system. Therefore this system will have a better performance dealing with emergencies in different surveillance scenarios. Moreover, this state-of-art intelligent video surveillance system not only brings a new method for surveillance, but also advances the development of surveillance techniques.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

With the development of video surveillance gradually from the traditional security surveillance to intelligent surveillance, in order to find an intelligent surveillance model to facilitate the implementation and promotion, we present a method, performing the moving object detection by using OpenCV based on DirectShow framework, which will make intelligent surveillance come true consequently. In this paper, it will first introduce a real-time priority method and some improvement of moving object detection. Secondly, the filter components that have moving object detection function will be designed by the combination method of DirectShow and OpenCV encapsulation technologies. Finally, it will present the design method and its advantages of building intelligent surveillance system on the basis of the above description.

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems.

Surveillance is one of the promising applications to which smart camera motes forming a vision-enabled network can add increasing levels of intelligence. We see a high degree of in-node processing in combination with distributed reasoning algorithms as the key enablers for such intelligent surveillance systems. To put these systems into practice still requires a considerable amount of research ranging from mote architectures, pixel-processing algorithms, up to distributed reasoning engines. This paper introduces MeshEye, an energy-efficient smart camera mote architecture that has been designed with intelligent surveillance as the target application in mind. Special attention is given to MeshEye's unique vision system: a low-resolution stereo vision system continuously determines position, range, and size of moving objects entering its field of view. This information triggers a color camera module to acquire a high-resolution image sub-array containing the object, which can be efficiently processed in subsequent stages. It offers reduced complexity, response time, and power consumption over conventional solutions. Basic vision algorithms for object detection, acquisition, and tracking are described and illustrated on real- world data. The paper also presents a basic power model that estimates lifetime of our smart camera mote in battery-powered operation for intelligent surveillance event processing.

The ability to automatically detect and track people is a key component of intelligent surveillance system (ISS). ISS is able to automatically analyze surveillance camera data with limited human intervention. In this research, robust people detection and tracking methods are studied for public surveillance system. The proposed method composed of foreground-background segmentation and people detection using mixture of gaussians (MOG) and histogram of oriented gradients (HOG) and then followed by multi object tracking (MOT) method for people tracking. The proposed method is tested on a closed circuit television (CCTV) system at the engineering College of Sebelas Maret University (UNS) Surakarta. All of the people tracking methods has been tested, and the result is good enough with the success up to 100%.

Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

In view of the frequent occurrence of current electric power theft accident in combination with existing domestic and foreign research status, a technology research of grid of intelligent security monitoring is proposed in this chapter. It describes the transmission line intelligent video surveillance system, analyzes the target detection algorithm, contrasts Kalman filter background modeling method and improves the target detection method, presents a case study with improved target detection algorithm, and is thus verified by the Halcon. This method has been successfully applied to the transmission line intelligent video surveillance system in Xinjiang Electric Power Company, 220 Kv. It provides a powerful guarantee for the power system security.

An intelligent video surveillance system was proposed together with distributed fiber vibration sensing technique and advanced intelligent identification algorithm, in order to monitor all kinds of real-time disturbance and intrusion on the cable.

The research for metadata extraction originates from the intelligent video surveillance system, which is widely used in outdoor and indoor environment for the aims of traffic monitor, security guard, and intelligent robot. Various features are extracted from the surveillance image sequences such as target detection, target tracking, object's shape and activities. However, the trend of more and more features being used and shared in video surveillance system calls for more attention to bridge the gap between specific analysis algorithms and end-user's expectation. This paper proposes a three-layer object oriented model to extract the surveillance metadata including shape, motion speed, and trajectory of the object emerging in image sequence. Meanwhile, the high-level semantic metadata including entry/exit point, object duration time is organized and stored which are provided for the further end-user queries. The paper also presents the experiment results in different indoor and outdoor surveillance scenarios. At last, a comparative analysis with another traditional method is presented.

Natural gas gathering station plays an important role in providing stable gas supply for downstream users. With the trend of the unattended station, the safety of the station becomes a major challenge. Intelligent video surveillance system, as an important part of unattended system, is mainly responsible for monitoring suspicious intruders. Target tracking technology is the key technology to achieve this goal. Off-line tracker is one of the main ways to achieve target tracking tasks. However, it requires a large number of samples for pre-training. Considering that human is the main monitoring object in intelligent video surveillance system, facial features can provide an effective basis for target tracking. Therefore, we introduce information maximizing generative adversarial nets as a generative model, which takes CelebA dataset as a benchmark to generate a large number of face samples with continuous change of feature attributes. Qualitative evaluation shows that the quality of synthetic face samples is high, which provides reliable data support for further training off-line tracker.

This paper presents a novel distributed intelligent video surveillance architecture based on Wireless Multimedia Sensor Networks (WMSNs). This architecture is part of a video surveillance project and has been built using the Robot Operating System (ROS). ROS allows to develop and connect (through wireless TCP-IP) several modules to process and manage multimedia data in an easy way. The real-time intelligent surveillance system has been trained for detecting, tracking and monitoring people and vehicles in an indoor-outdoor real environment. The test process shows the reliability of the developed system as a tool for the identification of security incidents. Besides, using wireless connections and a distributed architecture together, we have achieved a really flexible, easy to install and lower-maintenance system that supports many different devices. Thus, the proposed architecture can be applied in distributed locations such as smart cities.