Advanced Video Coding Systems

Gao, Wen; Ma, Siwei

doi:10.1007/978-3-319-14243-2

Cited by 11 publications

(3 citation statements)

References 150 publications

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“…In the XYZ color model, Y corresponds to relative luminance, Z is approximately equal to B dimension of CIE RGB, and X is a blend of all three. [ 71 ] Note that any color space with an explicit relationship to XYZ is said to be device independent. For example, once the three primary colors of an RGB space have determined values in XYZ color space, this RGB space becomes device independent.…”

Section: Color Space Transformationmentioning

confidence: 99%

Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

Fan

Liu

Miao

et al. 2023

Advanced Intelligent Systems

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The use of smartphone‐based analysis systems has been increasing over the past few decades. Among the important reasons for its popularity are its ubiquity, increasing computing power, relatively low cost, and capability to acquire and process data simultaneously in a point‐of‐need fashion. Furthermore, smartphones are equipped with various sensors, especially a complementary metal–oxide–semiconductor (CMOS) sensor. The high sensitivity of the CMOS sensor allows smartphones to be used as a colorimeter, fluorimeter, and spectrometer, constituting the essential part of point‐of‐care testing contributing to E‐health and beyond. However, despite its myriads of merits, smartphone‐based diagnostic devices still face many challenges, including high susceptibility to illumination conditions, difficulty in adapter uniformization, low interphone repeatability, and et al. These problems may hinder smartphone‐enabled diagnosis from passing the FDA regulations of medical devices. This review discusses the design and application of current smartphone‐based diagnostic devices and highlights challenges associated with existent methods and perspectives on how to deal with those challenges from engineering aspects on constant color signal acquisition, including smartphone adapter design, color space transformation, machine learning classification, and color correction.

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Section: Color Space Transformationmentioning

confidence: 99%

Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

Fan

Liu

Miao

et al. 2023

Advanced Intelligent Systems

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“…ISS includes many intelligent analysis modules, e.g., video motion detection, camera tampering detection, people counting, face recognition, vehicle license plate recognition, and so on. Also intelligent video systems not only provide realtime abnormal event detection but also acquire continuous video sequences from adjacent cameras using panning, tilting, and zooming (Pan-Tilt-Zoom (PTZ)) with multichannel, multi-area, and an immediate acquisition of the monitoring area video with complex and vast environment, which needs lots of complex computation and is almost impossible for people to finish in real time [1]. Some related researches that investigating the detection of anomaly behavior in crowed scene are presented in [2] [3] [4] [5] [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Anomalous Behavior Detection Using the Geometrical Complex Moments in Crowd Scenes of Smart Surveillance Systems

Shati¹

2018

MJS

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In this research work a data stream clustering method done by extracting regions of interest from the frames of video clips (UCSD pedestrian dataset (ped1 and ped2 datasets) video clips, and VIRAT VIDEO dataset video clips). In extraction process, the HARRIS or FAST detector applied on the frames of video clips to extract list of pairs of interest points. From these pairs a list of features such as: distance, direction, x-coordinate, y-coordinate obtained to use as an input to the clustering method based on seed based region-growing technique. From these clusters, a regions of interest extracted according the pairs coordinates of each cluster. Finally, from these regions a set of geometrical complex moments obtained and then used in anomaly detection system. The results indicated that using HARRIS detector achieved detection rates are 7.88%, 51.30%, and 56.67% with false alarms are 19.39%, 32.61%, and 60 % by using Ped1, Ped2, and VIRAT datasets respectively. For the case of using FAST detector, the best detection rates are 6.67%, 44.78%, 53.33% with false alarm rates are 33.33%, 41.74%, 70% by using the datasets respectively. Keywords

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“…To relieve the financial pressure, normally a high compression ratio is adopted to make a trade‐off between the retention period and the video quality. However, according to previous research , the reduced quality of surveillance video severely degrades the performance in video analysis tasks such as object recognition and tracking. This makes critical applications such as large crowd management in public hotspot areas very unreliable .…”

Section: Introductionmentioning

confidence: 99%

A sensitive object‐oriented approach to big surveillance data compression for social security applications in smart cities

et al. 2016

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Surveillance has become a fairly common practice with the global boom in "smart cities". How to efficiently store and manage the vast quantities of surveillance data is a persistent challenge in terms of analyzing social security problems. Developing data compression technology under the analytic requirements of surveillance data is the key to solving the storage problem. Criminal investigation demands the quality preservation of sensitive objects, typically pedestrians, human faces, vehicles, and license plates; however, the analytical value of surveillance data is rapidly lost as the compression ratio increases. In this paper, we propose a sensitive object-oriented regions of interest-based coding strategy for preserving the analytical value of surveillance data. In the proposed method, instead of generating a saliency map based on human visual perception, we consider saliency as a set of characteristics important for object detection and recognition. By making this modification, almost all sensitive objects necessary in a criminal investigation are assigned high saliency value rather than only one or two salient regions. Motions in the temporal domain are integrated to place emphasis on moving objects, namely moving sensitive objects, which then gain the highest saliency. Finally, a saliency-based rate control algorithm embedded in High Efficiency Video Coding is used to maintain the quality of sensitive objects in the encoded video under a fixed bitrate. Experiments were conducted on two analytical indexes: Feature similarity and object detection accuracy. The results showed that by achieving the same feature similarity and object detection accuracy, our method can save 20% and 40% bitrate over High Efficiency Video Coding, respectively, for the storage of big surveillance data. be archived. Depending on the retention policy, video streams (which are in increasingly higher resolution) can consume storage rapidly, leading to high financial pressure to scale the storage infrastructure. To relieve the financial pressure, normally a high compression ratio is adopted to make a trade-off between the retention period and the video quality. However, according to previous research 1, the reduced quality of surveillance video severely degrades the performance in video analysis tasks such as object recognition and tracking. This makes critical applications such as large crowd management in public hotspot areas very unreliable 2.Our ability to maintain video analysis performance while resolving the surveillance video storage problem is dependent on our ability to accurately and effectively account for the characteristics of the videos. In normal applications, surveillance videos are used for analytical purposes. Taking a typical criminal investigation application, for instance, the analytically sensitive objects (e.g., pedestrians and vehicles) in the videos have much higher importance than the background. Therefore, it is reasonable to divide surveillance video frames into regions of interest (ROIs) and non-ROIs where R...

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Advanced Video Coding Systems

Cited by 11 publications

References 150 publications

Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

Anomalous Behavior Detection Using the Geometrical Complex Moments in Crowd Scenes of Smart Surveillance Systems

A sensitive object‐oriented approach to big surveillance data compression for social security applications in smart cities

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