A Retrieval Optimized Surveillance Video Storage System for Campus Application Scenarios

Ma, Shengcheng; Chen, Xin; Li, Zhuo; Yang, Yan

doi:10.1155/2018/3839104

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…A common approach includes the coupling of Video surveillance and Cloud Storage; which entails large-scale video networking across urban nodes or the entire urban fabric. As Ma et al [31] explain, this concept has also been adopted in smaller urban developments and has allowed for quicker and efficient ways of extracting content from video recordings and for surveillance. This approach is supported by other technologies like the Wireless Broadband Trunking and working with 4G [32,33].…”

Section: The Safe Citymentioning

confidence: 99%

The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City

Allam

2019

Smart Cities

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The emergence of Big Data, accelerated through the Internet of Things (IoT) and Artificial Intelligence, from the emerging, contemporary concept of smart cities coupled with that of the notion for safe cities is raising concerns of privacy and good governance that are impacting on socio-economic and liveability dimensions of urban fabrics. As these gain ground, largely due to economic pressures from large ICT providers, there is a notable increase towards the need for inclusion of human dimensions, complemented by the use of technology. However, the latter is seen as catalysing elements of control and propaganda which are thriving through oversimplified and non-inclusive urban IT policy measures. This paper dwells on the intersecting subjects of smart and safe cities and explores the highlighted issues that are deemed to cause concern and further explore the need for transparency and inclusivity in urban processes and systems. This paper is oriented towards urban planners and policy makers looking at the implementation of smart and safe cities concepts.

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Section: The Safe Citymentioning

confidence: 99%

The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City

Allam

2019

Smart Cities

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“…Surveillance cameras produce large numbers of videos with multiple data types at a low value density and a strong real-time performance. There are many challenges to security inspection [1], [28], [29], [37], [49]. However, the shooting range of current monitoring systems cannot cover all key monitoring areas, while in general, the areas monitored by multiple cameras do not overlap.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Re-Identification Monitoring System Based on Deep Convolutional Neural Network

Qu¹,

Xu²,

Luo³

et al. 2020

IEEE Access

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The gradual establishment of large-scale distributed camera networks and the rapid development of ''Internet +'' have resulted in the recent popularization of massive video surveillance systems. As pedestrians are the key monitoring targets in video surveillance systems, many studies are focusing on pedestrian re-identification monitoring algorithms across cameras. At present, the pedestrian re-identification model is not only faced with the difficulty of training the network model due to the huge quantity difference between different types of training samples, but also needs to reduce the impact of the large difference in visual performance on the model identification accuracy. To solve these difficulties, this paper proposed a deep learning model and designed a system based on a deep convolutional neural network for pedestrian re-identification. In particular, we determined the difference between the system input neighborhoods in order to derive the local relationship between the two input images, thus reducing the effects of illumination and perspective. Furthermore, we employed focal loss to solve the phenomenon of sample imbalance in the pedestrian re-identification process in order to enhance the actual application potential of the model. The proposed method was implemented in our developed end-to-end monitoring system for pedestrian re-identification. The hardware component of the system design framework was composed of a digital matrix, streaming media storage server and a network high-speed dome, with the ability to extend to additional tasks in the future. Our approach reduces the effects of data imbalances and visual performance differences, with a score of 76.0% for rank-1 and 99.5% for rank-20 on large data sets (CUHK03), which is not only a significant improvement over the previous IDLA, but also superior to other existing approaches.INDEX TERMS Deep convolution, monitoring system, neural network, pedestrian re-identification.

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“…In [18] Dimitrious et al have presented modular surveillance model based on embedded computing technique for efficient video analytics solution for detecting minor crimes. Ma et al [19] have presented optimal storage approach based on key-indexing to extract the useful content in efficient way. The experimental outcome of presented study shows that it achieves good performance for extortion of information from surveillance video content.…”

Section: Introductionmentioning

confidence: 99%

Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

Chandra

M²

2019

IJECE

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An effective video surveillance system is highly essential in order to ensure constructing better form of video analytics. Existing review of literatures pertaining to video analytics are found to directly implement algorithms on the top of the video file without much emphasis on following problems i.e. i) dynamic orientation of subject, ii)poor illumination condition, iii) identification and classification of subjects, and iv) faster response time. Therefore, the proposed system implements an analytical concept that uses depth-image of the video feed along with the original colored video feed to apply an algorithm for extracting significant information about the motion blob of the dynamic subjects. Implemented in MATLAB, the study outcome shows that it is capable of addressing all the above mentioned problems associated with existing research trends on video analytics by using a very simple and non-iterative process of implementation. The applicability of the proposed system in practical world is thereby proven.

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A Retrieval Optimized Surveillance Video Storage System for Campus Application Scenarios

Cited by 5 publications

References 15 publications

The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City

The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City

Pedestrian Re-Identification Monitoring System Based on Deep Convolutional Neural Network

Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

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