Self-organized night video enhancement for surveillance systems

Soumya, T; Thampi, Sabu M.

doi:10.1007/s11760-016-0893-6

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…At present, video image enhancement algorithms can be divided into machine learning and non-machine learning. Using machine learning to enhance video images can usually achieve good results in large data sets, but this method also has its own shortcomings [1][2]. First, it needs a lot of data to train.…”

Section: Introductionmentioning

confidence: 99%

Low Illumination Video Image Enhancement

Yang

Kasabov

2020

IEEE Photonics J.

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Due to weather conditions, brightness conditions, capture equipment and other factors, leads to video unclear or even abnormally confused, which is not conducive to monitoring, and can not meet the needs of applications. Based on the actual data of night video surveillance, this paper proposes a new low illumination video image enhancement algorithm, which overcomes the existing problems. We analyze the characteristics of low illumination video image, and use HSV color space instead of traditional RGB space to enhance the robustness of video contrast and color distortion. At the same time, we use wavelet image fusion to highlight the details of video image, so the enhanced video has higher clarity and visual effect. Compared with other four algorithms, the proposed algorithm outperforms the above algorithms in subjective evaluation and objective evaluation. At the same time, compared with other algorithms, the proposed algorithm has faster processing time for each frame. Experiments show that the algorithm can effectively improve the overall brightness and contrast of video images, and avoid the over-enhancement of bright areas near the light source, which can meet the practical application requirements of video surveillance.

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Section: Introductionmentioning

confidence: 99%

Low Illumination Video Image Enhancement

Yang

Kasabov

2020

IEEE Photonics J.

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“…Works presented in [16]- [19] are sources of inspiration to integrate the event-driven awakening feature into the proposed system. The proposed solution also possesses an interesting feature of self-organization [20], [36]. Its pattern of monitoring and processing is adapted according to activities of humans within the system range.…”

Section: Introductionmentioning

confidence: 99%

An Event-Driven Multiple Objects Surveillance System

Qaisar

Sidiya

Akbar

et al. 2018

Int. j. electr. comput. eng. syst. (Online)

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Traditional surveillance systems are constrained because of a fixed and preset pattern of monitoring. It can reduce the reliability of the system and cause an increased generation of false alarms. It results in an increased processing activity of the system, which causes an augmented consumption of system resources and power. Within this framework, a human surveillance system is proposed based on the event-driven awakening and self-organization principle. The proposed system overcomes these downsides up to a certain level. It is achieved by intelligently merging an assembly of sensors with two cameras, actuators, a lighting module and cost-effective embedded processors. With the exception of low-power event detectors, all other system modules remain in the sleep mode. These modules are activated only upon detection of an event and as a function of the sensing environment condition. It reduces power consumption and processing activity of the proposed system. An effective combination of a sensor assembly and a robust classifier suppresses generation of false alarms and improves system reliability. An experimental setup is realized in order to verify the functionality of the proposed system. Results confirm proper functionality of the implemented system. A 62.3-fold system memory utilization and bandwidth consumption reduction compared to traditional counterparts is achieved, i.e. a result of the proposed system self-organization and event-driven awakening features. It confirms that the proposed system outperforms its classical counterparts in terms of processing activity, power consumption and usage of resources

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Image Processing-Based Electronic Fence: A Review

Wang

Huang

Zhao

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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With the development of science and technology, using electronic fence to replace traditional physical fence becomes a trend. Research in the image processing-based electronic fence is gaining more popularity due to its low cost, low power consumption, and intelligence. In this paper, we are interested in the study of three types of scenarios with an image processing-based electronic fence. The scenarios contains national border protection, safety management in manufacturing field and vehicles safety plans. The state of the art frameworks of the three scenarios are reviewed, and the trend of these fields is also discussed.

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Self-organized night video enhancement for surveillance systems

Cited by 7 publications

References 19 publications

Low Illumination Video Image Enhancement

Low Illumination Video Image Enhancement

An Event-Driven Multiple Objects Surveillance System

Image Processing-Based Electronic Fence: A Review

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