https://www.mdpi.com/2076-3417/14/1/408
This approach not only improves current security camera analysis but also shows potential for diverse application settings, signifying a significant advancement in the evolution of security camera monitoring and analysis technologies. Keywords: deep learning; Transformers; video surveillance; anomaly detection; RNN. 1.

https://www.sciencedirect.com/science/article/pii/S0262885621001347
Design an intelligent IVADC-FDRL model for video anomaly detection and classification. • Propose a Faster RCNN with ResNet as a baseline model for Anomaly Detection. • Employ a deep Q-learning (DQL) model to classify detected anomalies in video frames. • Validate the anomaly detection and classification performance on UCSD Anomaly dataset

https://www.youtube.com/watch?v=AMvhzySB3CY
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https://link.springer.com/article/10.1007/s11042-020-09964-6
An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. ... When performing detection using VGG-16, Fast R-CNN can be 3× faster than SPPnet and 9× ... Ouyang W, Wang X, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. In: International

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0212-5
Big data applications are consuming most of the space in industry and research area. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. Surveillance videos have a major contribution in unstructured big data. CCTV

https://www.nature.com/articles/s41598-024-54428-8
Target detection is accomplished in real time and with excellent precision. In the realm of video surveillance 3, it combines adaptive background modeling to detect moving objects in videos

https://www.mdpi.com/1424-8220/23/11/5024
Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There

https://www.sciencedirect.com/science/article/pii/S0262885620302109
Deep learning-based video anomaly detection methods provide high detection accuracy at the expense of computational as well as space complexity. Hence, there is a need for hardware and algorithms (software) co-design [177], [178] for attaining online performance without compromising on the detection accuracy. 9.6.

https://ieeexplore.ieee.org/document/10128302
Technologies that are used in automated video surveillance give the capacity of automatically identifying security breaches or other potentially dangerous occurrences taking place inside the field of view of the cameras. These technologies have applications in the areas of surveillance as well as the detection of intrusions along perimeters. Recognizing anomalous behavior in crowded

https://link.springer.com/chapter/10.1007/978-3-030-89554-9_2
Latest strategies use CNN for visual detection—a deep learning algorithm of convolution layer as the framework of the model. ... Object classification based on image blobs is used by video surveillance and monitoring (VSAM) (Collins ... IoT-enabled drones can detect, locate, and track a moving human target automatically. The framework focuses

https://www.sciencedirect.com/science/article/pii/S2666281722000154
In recent years, with the success of deep learning on image classification (Krizhevsky et al., 2012; Simonyan and Zisserman, 2014) and object detection tasks (Girshick et al., 2014; Ren et al., 2016), deep learning-based supervised, unsupervised, and semi-supervised techniques are also gaining popularity for video anomaly detection.

https://www.researchgate.net/publication/365000356_Object_Detection_and_Classification_Algorithms_using_Deep_Learning_for_Video_Surveillance_Applications
Object Classification is a principle task in image and video processing. It is exercised over a multitude of applications ranging from test and number classification to traffic surveillance.

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://www.mdpi.com/2571-6255/6/8/315
Among various calamities, conflagrations stand out as one of the most-prevalent and -menacing adversities, posing significant perils to public safety and societal progress. Traditional fire-detection systems primarily rely on sensor-based detection techniques, which have inherent limitations in accurately and promptly detecting fires, especially in complex environments. In recent years, with

https://ieeexplore.ieee.org/document/8610075
In recent years, deep learning algorithms have achieved top performances in object detection tasks. However, in real-time, systems having memory or computing limitations very wide and deep networks with numerous parameters constitute a major obstacle. In this paper, we propose a fast method for detecting pedestrians in surveillance systems having limited memory and processing units. Our

https://www.researchgate.net/publication/356782230_Improved_Object_Detection_in_Video_Surveillance_Using_Deep_Convolutional_Neural_Network_Learning
Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection.

https://www.researchgate.net/publication/365001145_Object_Detection_and_Tracking_using_Deep_Learning_and_Artificial_Intelligence_for_Video_Surveillance_Applications
Objects are tracked across the frames using YOLOv3 and Simple Online Real Time Tracking (SORT) on traffic surveillance video. This paper upholds the uniqueness of the state of the art networks

https://www.sciencedirect.com/science/article/pii/S2665917422000563
Recently, person detection and tracking in a video scene of a surveillance system were grabbing higher interest because of its extensive range of applications in gender classification, abnormal event recognition, person identification, human gait characterization, individual counting in a dense crowd, and fall detection for older people, etc.

https://deepai.org/publication/object-detection-and-classification-algorithms-using-deep-learning-for-video-surveillance-applications
Object Classification is a principle task in image and video processing. It is exercised over a multitude of applications ranging from test and number classification to traffic surveillance. The primitive machine learning concepts had provided the pedestal for carrying out umber of image processing tasks. Nowadays requirement of detection

https://link.springer.com/article/10.1007/s00500-023-08289-4
Pedestrian detection is crucial for crowd surveillance applications and cyber-physical systems that can deliver timely and sophisticated solutions, especially with applications like person identification, person count, and tracking as the number of people rises. Even though the number of cutting-edge neural network-based frameworks for object detection models and pedestrian detection in images

https://ijcrt.org/papers/IJCRT2207445.pdf
An intelligent monitoring pro-gram that uses "Image Captioning" with DL and data analytics to dramatically improve the pre-existing surveillance system for smart crime detection. Our software can execute ImageCaptioning on several CCTV clips and save the captions,as well as the capture time, in a handy log.

https://www.mdpi.com/2079-9292/13/13/2579
Detecting abnormal human behaviors in surveillance videos is crucial for various domains, including security and public safety. Many successful detection techniques based on deep learning models have been introduced. However, the scarcity of labeled abnormal behavior data poses significant challenges for developing effective detection systems. This paper presents a comprehensive survey of deep

https://www.researchgate.net/publication/328701453_Deep_Learning-Based_Person_Detection_and_Classification_for_Far_Field_Video_Surveillance
This paper presents a deep learning-based approach to detect and classify persons in video data captured from distances of several miles via a high-power lens video camera. For detection, a set of

https://dl.acm.org/doi/abs/10.1016/j.engappai.2023.107513
Simulated photorealistic deep learning framework and workflows to accelerate computer vision and unmanned aerial vehicle research. ... Guo Y., Lu Y., Zhu F., Huan Y., Liu R.W., Intelligent maritime surveillance framework driven by fusion of camera-based vessel detection and ais data, in ... A guide to image and video based small object

https://www.nature.com/articles/s41598-024-65885-6
In the context of illicit traffic of CH goods, Winterbottom et al. 35 developed a machine learning-based framework for instance classification of large archaeological image datasets. They focused

https://www.researchgate.net/publication/343963531_Fast_Learning_Through_Deep_Multi-Net_CNN_Model_For_Violence_Recognition_In_Video_Surveillance
The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition.

https://dl.acm.org/doi/10.1016/j.aei.2023.102334
Therefore, this study proposes a blockchain deep learning framework that focuses on how to efficiently extract and securely store key information (i.e., video summarization that involves worker's unsafe behavior) on-blockchain for data traceability.

https://link.springer.com/article/10.1007/s11042-024-19696-6
Video tampering allows an intruder to alter any video on the basis of pixel and frame level to perform any criminal activity. In Fig. 6, examples of tampering detection in video surveillance scenarios have been highlighted. It motivates several investigators to develop video tampering detection systems defined in the following section.

https://link.springer.com/article/10.1007/s11760-024-03401-z
2.2 Human moving target detection algorithm. Dalal et al. proposed a gradient variation-based feature description, the HOG feature. The HOG feature describes an image with a gradient and edges and mainly focuses on the histogram statistics of the local region of the image [].For human targets, the edge contour feature is a very important feature description, so the HOG feature is suitable for

https://link.springer.com/article/10.1007/s10462-024-10816-0
Deep learning models have been extensively utilized in various applications to perform classification tasks on many data types, including images, text, and audio. This approach has been demonstrated to be highly effective and influential. Deep learning models can extract task-specific information from large datasets.