Management of Video Surveillance for Smart Cities

2018 ◽  
pp. 285-310
Author(s):  
Nhat-Quang Dao ◽  
Quang Le-Dang ◽  
Robert Morawski ◽  
Anh-Tuan Dang ◽  
Tho Le-Ngoc
Keyword(s):  
Video Surveillance ◽  
Smart Cities

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

scholarly journals Application of region-based video surveillance in smart cities using deep learning

2021 ◽  
Author(s):  
Asma Zahra ◽  
Mubeen Ghafoor ◽  
Kamran Munir ◽  
Ata Ullah ◽  
Zain Ul Abideen
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
Video Surveillance ◽  
Smart City ◽  
Smart Cities ◽  
Video Frame ◽  
Visual Data ◽  
High Quality ◽  
City Environment ◽  
Salient Regions

AbstractSmart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities.

scholarly journals Surveillance using Face Recognition in Smart Cities

2020 ◽  
Vol 9 (4) ◽  
pp. 2042-2047
Keyword(s):  
Face Recognition ◽  
Video Surveillance ◽  
Crime Rate ◽  
Smart Cities ◽  
Police Department ◽  
Indian Government ◽  
The Past ◽  
Time To Build ◽  
Voting System ◽  
The City

In this project safe city demonstrates how the security in India can be increased with the help of video surveillance using facial recognition. In the Aadhar Card database, the Indian Government has stored fingerprint and Iris details of every civilian in India. But the Indian Government is only using the Fingerprint details in the voting system to avoid fake votes. With the help of this project any person roaming in the city limit can be easily monitored. This will be a very useful technology for the Police Department of India to track the criminals and to reduce crime rate. Whenever a person or criminal is needed to be traced , the photo of the target is uploaded into the software. The uploaded photo will be cross-checked by the software with the videos captured from the surveillance cameras. It will then identify the person based on the percentage of accuracy to be matched. In the past 5 years Indian Government have made many cities into smart cities. But now it’s time to build safe cities for India.

A dynamic, plug-and-play and efficient video surveillance platform for smart cities

Kahn at Penn ◽  
2017 ◽  
Author(s):  
G. Baldoni ◽  
M. Melita ◽  
S. Micalizzi ◽  
C. Rametta ◽  
G. Schembra ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Smart Cities ◽  
Plug And Play ◽  
Efficient Video

scholarly journals Video Surveillance-Based Intelligent Traffic Management in Smart Cities

2019 ◽  
Author(s):  
Fozia Mehboob ◽  
Muhammad Abbas ◽  
Abdul Rauf ◽  
Shoab A. Khan ◽  
Richard Jiang
Keyword(s):  
Video Surveillance ◽  
Traffic Management ◽  
Smart Cities

scholarly journals A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

2020 ◽  
Vol 10 (18) ◽  
pp. 6572
Author(s):  
Chel-Sang Yoon ◽  
Hae-Sun Jung ◽  
Jong-Won Park ◽  
Hak-Geun Lee ◽  
Chang-Ho Yun ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Communication Technology ◽  
Smart City ◽  
Smart Cities ◽  
Video Data ◽  
Smart Services ◽  
Video Cameras ◽  
Video Images ◽  
Information And Communication ◽  
City System

A smart city is a future city that enables citizens to enjoy Information and Communication Technology (ICT) based smart services with any device, anytime, anywhere. It heavily utilizes Internet of Things. It includes many video cameras to provide various kinds of services for smart cities. Video cameras continuously feed big video data to the smart city system, and smart cities need to process the big video data as fast as it can. This is a very challenging task because big computational power is required to shorten processing time. This paper introduces UTOPIA Smart Video Surveillance, which analyzes the big video images using MapReduce, for smart cities. We implemented the smart video surveillance in our middleware platform. This paper explains its mechanism, implementation, and operation and presents performance evaluation results to confirm that the system worked well and is scalable, efficient, reliable, and flexible.

scholarly journals Integrated Multiscale Appearance Features and Motion Information Prediction Network for Anomaly Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ting Liu ◽  
Chengqing Zhang ◽  
Liming Wang
Keyword(s):  
Anomaly Detection ◽  
Optical Flow ◽  
Video Surveillance ◽  
Short Term Memory ◽  
Smart Cities ◽  
Receptive Fields ◽  
Motion Information ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Flow Loss

The rise of video-prediction algorithms has largely promoted the development of anomaly detection in video surveillance for smart cities and public security. However, most current methods relied on single-scale information to extract appearance (spatial) features and lacked motion (temporal) continuity between video frames. This can cause a loss of partial spatiotemporal information that has great potential to predict future frames, affecting the accuracy of abnormality detection. Thus, we propose a novel prediction network to improve the performance of anomaly detection. Due to the objects of various scales in each video, we use different receptive fields to extract detailed appearance features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the motion information between consecutive frames. Furthermore, we use RGB difference loss to replace optical flow loss as temporal constraint, which greatly reduces the time for optical flow extraction. Compared with the state-of-the-art methods in the anomaly-detection task, experiments prove that our method can more accurately detect abnormalities in various video surveillance scenes.

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