https://www.mdpi.com/2313-7673/8/6/480
Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges such as slow detection

https://link.springer.com/article/10.1007/s11042-023-17102-1
In Section 2, we discuss recent work on object detection for intelligent video surveillance systems. ... (2021) is a real-time tracking system that does segmentation of the target individual by combining a mask branch with a fully convolutional twin neural network. Individual video sequences from the previous section are selected, and transfer

https://link.springer.com/article/10.1007/s11042-018-6871-y
Target tracking is one of the pivotal technologies in intelligent video surveillance systems. Facing the complex and various scenarios in practical applications, improving the accuracy and real-time of target detection and tracking is has become the goal of current monitoring systems. Firstly, the target feature expression model is established by fusing Sobel Median Binary Pattern (SMBP) and H

https://ieeexplore.ieee.org/document/8101390
Moving target detection is an important part of intelligent video surveillance system, in which background difference method and inter-frame difference method are the two most commonly used methods. This paper has analyzed the principle and algorithm of these two methods, the program simulation is realized based on open source platform of OpenCV. The experimental results show that the two

https://link.springer.com/chapter/10.1007/978-3-030-89554-9_2
Dim and weak target detection technology based on multi-characteristic fusion. In Proceedings of the 26th conference of spacecraft TT&C technology,Beijing, China, pp. 271-277. ... Understanding New Age of Intelligent Video Surveillance and Deeper Analysis on Deep Learning Techniques for Object Tracking. In: Rodrigues, J.J.P.C., Agarwal, P

https://arxiv.org/abs/2110.15674
Deep learning models are known to function like the human brain. Due to their functional mechanism, they are frequently utilized to accomplish tasks that require human intelligence. Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researcher's attention due to its potential applications in various domains. Multi-target

https://onlinelibrary.wiley.com/doi/full/10.4218/etrij.15.0114.0629
Intelligent video surveillance systems are increasingly employed in the field of security. Among several core technologies of intelligent video surveillance, automatic and robust real-time tracking of multiple people is essential. ... These algorithms approach the problem of multi-target tracking by optimizing detection assignments over a

https://www.sciencedirect.com/science/article/pii/S0045790621002445
With the rapid development of network technology, UAV image target detection has a wide range of applications, including intelligent video surveillance, forest fire prevention, agricultural information, power line detection, archaeological studies, road and bridge damage assessment, and military reconnaissance.

http://www.jatit.org/volumes/Vol49No2/23Vol49No2.pdf
workflow of intelligent video surveillance system is shown in Figure 1. The entire system mainly consists of four parts, target detection, target classification, target tracking and target behavior recognition. Target detection is the key step of the intelligent video surveillance, the results of this process is the basis of all the post

https://onlinelibrary.wiley.com/doi/10.1155/2021/9470895
An intelligent vision-enabled water-surface target detection framework with deep neural networks has been proposed for IoT-based maritime video surveillance (2) Optimal strategies for training deep neural networks have been represented to handle the influences of different severe weather conditions on water-surface target detection

https://dl.acm.org/doi/10.1007/s42979-022-01654-4
A review on object detection and tracking in video surveillance, Sanjeevkumar Angadi Assistant Professor, MIT College of Railway Engineering and Research, Barshi, Maharashtra, India Suvarna Nandyal Professor, Poojya Doddappa Appa College of Engineering, Gulbarga, Karnataka, India, International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 9, September

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0212-5
Bouachir W, Gouiaa R, Li B, Noumeir R. Intelligent video surveillance for real-time detection of suicide attempts. Pattern Recogn Lett. 2018;110:1-7 (ISSN 0167-8655). Article Google Scholar Wang J, Xu Z. Spatio-temporal texture modelling for real-time crowd anomaly detection.

https://link.springer.com/article/10.1007/s11082-023-05664-1
Video surveillance frameworks have become fundamental for guaranteeing public well-being and security in different environments, including air terminals, public transportation, and foundation offices (Khan and Han 2018; Li et al. 2020).With the appearance of computerized camcorders and high-velocity organizations, far-off observation has become an undeniably famous way to deal with screens and

https://ijiemr.org/public/uploads/paper/426821651317911.pdf
Methods for Crowd analysis and violence detection are also included. 1.3 Scope of the Project Intelligent Video Surveillance is a rapidly growing industry which is used in Remote video monitoring: To protect against theft, burglaries, and dishonest employees. Facility Protection: To protect the perimeter of the property or the perimeter of

https://bit.kuas.edu.tw/~jihmsp/2020/vol1/5_jihmsp-1568_vol4.pdf
How to apply intelligent video surveillance to border defense systems to meet the requirements of practical, effective, reliable, advanced and econom- ... Finally, image and video target detection tests are carried out on the model generated by training. Keywords: Target detection, Target recognition, Deep learning, MobileNet-SSD algo-

https://opticalengineering.spiedigitallibrary.org/conference-proceedings-of-spie/9301/93010B/A-moving-target-detection-algorithm-based-on-GMM-and-improved/10.1117/12.2068911.full
Based on Gaussian mixture model, an improved detection algorithm, which aimed at updating the real-time character and accuracy of the moving target detection in intelligent video surveillance systems effectively, is elaborated in this paper. It combines the advantages of GMM and improved maximum between class variance method. The algorithm not only improves the speed of detecting targets in

https://arc.aiaa.org/doi/10.2514/1.I010198
Unmanned air systems with video capturing systems for surveillance and visual tracking of ground targets have worked relatively well when employing gimbaled cameras controlled by two or more operators: one to fly the vehicle, and one to orient the camera and visually track ground targets. However, autonomous operation to reduce operator workload and crew levels is more challenging when the

https://www.sciencedirect.com/science/article/pii/S0957417417306334
Indeed, the detection of an abnormal behavior in video surveillance is essential to ensure safety in both outdoor and indoor places such as train stations and airports. In fact, abnormal behavior detection is a particular problem of human action recognition. With the increasing number of surveillance cameras, the task of supervising multiple

https://www.researchgate.net/publication/373759850_Review_of_Target_Detection_Algorithms
Object detection is a popular direction of computer vision and digital image processing, which is widely used in robot navigation, intelligent video surveillance, industrial detection, aerospace

https://ijrpr.com/uploads/V4ISSUE3/IJRPR10470.pdf
Intelligent Surveillance is the use of automatic videotape analytics to enhance effectiveness of surveillance systems. This system introduces automatic identification of persons exertion to enhance the security system in sanatorium this enriches the current video surveillance systems. The applicable data is captured and alert is given to the

https://link.springer.com/article/10.1007/s42979-022-01654-4
Various methods have being used in tracking and detecting humans in videos. Some of the popular methods include HOG (Histogram of Oriented Gradients as feature extractor) with salience-windowed frames of the video to the HOG [], HOG application example of ATM Video Surveillance system with SVM Classifier, and NMS algorithm results better performance for human detection of accuracy 97% [], a

https://www.dahuawiki.com/IVS
IVS - Intelligent Video System About. The Face Capture, Perimeter Protection, and IVS functions cannot be used at the same time, only one Analytics+ or IVS function can be used at a time. ... Reduce the complexity of surveillance scene as possible as you can. Analytics analysis function is not recommended to be used in scene with dense targets

https://ui.adsabs.harvard.edu/abs/2024arXiv240618815Y/abstract
In the context of escalating safety concerns across various domains, the tasks of Video Anomaly Detection (VAD) and Video Anomaly Recognition (VAR) have emerged as critically important for applications in intelligent surveillance, evidence investigation, violence alerting, etc. These tasks, aimed at identifying and classifying deviations from normal behavior in video data, face significant

https://link.springer.com/article/10.1007/s11042-020-09964-6
Therefore, video object detection is suitable to be considered as part of the future improvements for the proposed video surveillance system. Furthermore, it is discussed in papers including [ 3 , 7 , 42 , 45 ] that it is possible to achieve impressive accuracies with video salient object detection (VSOD) based on the visual attention mechanism.

https://www.mdpi.com/2076-3417/14/13/5587
Technologies for the detection of dim and small targets in infrared images play an increasingly important role in various applications, including military early warning, precise guidance, military reconnaissance, environmental monitoring, and aerospace applications. This paper proposes a new approach for the detection of infrared multi-scale small targets based on a feature pyramid network.