Image-Based Ciphering of Video Streams and Object Recognition for Urban and Vehicular Surveillance Services

Hammoudi, Karim; Taha, Mohammed A.; Benhabiles, Halim; Windal, Féryal; Assad, Safwan El; Queudet, Audrey

doi:10.1007/978-981-32-9343-4_42

Cited by 2 publications

(4 citation statements)

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“…In GOG and HGD, in addition to RGB color space, the LAB, HSV, and nRGB color spaces are also used by substituting their color channels with RGB information (i.e., 𝑥 𝑅 , 𝑥 𝐺 , and 𝑥 𝐵 ) used in Equation (1). We name the extracted feature vectors using LAB, HSV, and nRGB 1 , as 𝑍 𝐿𝐴𝐵 , 𝑍 𝐻𝑆𝑉 , and 𝑍 𝑛𝑅𝐺𝐵 respectively.…”

Section: People Re-identificationmentioning

confidence: 99%

“…According to Table 1, we can apply our proposed biasing scheme on descriptors by multiplying the factors on the extracted features. Hence, biasing the descriptors imposes the constant overhead as 𝑂 (1).…”

Section: Prid 450smentioning

confidence: 99%

“…INTRODUCTION 1 Surveillance systems with a network of cameras often use several video cameras with non-overlapping views. These systems are used to analyze people's behavior and ultimately detect abnormal events in public and private places such as transport systems [1], government offices [2], and smart buildings in which elder citizens are cared after [3]. People re-identification is a challenging issue in surveillance systems, which directly affects their performance.…”

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confidence: 99%

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Sensing Image Regions for Enhancing Accuracy in People Re-identification

Hassanpour

Mortezaie

Beghdadi³

2022

IJEE

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Video surveillance systems are widely used in the public and private sectors for maintaining security and healthcare purposes. Performance of surveillance systems directly depends on their accuracy in re-identification. There are three regions in a camera view, including person's body, background, and possible carried object by the person. Background, in existing approaches, is either overlooked or treated like a person's body in re-identification. In this paper, these three regions are considered in re-identification but with different importance. In our proposed technique, first, the input image is semantically segmented into the three regions using a deep semantic segmentation approach. Then, the effect of each region on characteristic features of people is tuned depending on the region's importance in re-identification. The proposed technique, leveraging robust descriptors, such as the Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGD), can enhance existing methods in dealing with the challenging issues such as partial occlusion caused by carried objects and background in re-identification. Experimental results on commonly used people re-identification datasets demonstrate effectiveness of the proposed technique in improving performance of existing re-identification methods.

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Section: People Re-identificationmentioning

confidence: 99%

Section: Prid 450smentioning

confidence: 99%

mentioning

confidence: 99%

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Sensing Image Regions for Enhancing Accuracy in People Re-identification

Hassanpour

Mortezaie

Beghdadi³

2022

IJEE

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“…For the tests, 6,000 individual parking spaces of the UFPR04 subset were used. Hammoudi et al (2018aHammoudi et al ( ,b, 2019Hammoudi et al ( , 2020) also proposed the use of LBP-based features to classify parking lot images. In these four quite similar works, the authors used a k-NN classifier and small image subsets (3,000 to 6,000 segmented images) of the PKLot for the tests.…”

Section: Individual Parking Spaces Classificationmentioning

confidence: 99%

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

de Almeida,

Alves,

Parpinelli

et al. 2022

Preprint

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Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently present a systematic and comprehensive review of existing works that employ such datasets. The literature review identified relevant gaps that require further research, such as the requirement of dataset-independent approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have noticed that several important factors such as the presence of the same cars across consecutive images, have been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis of the datasets also revealed that certain features that should be present when developing new benchmarks, such as the availability of video sequences and images taken in more diverse conditions, including nighttime and snow, have not been incorporated.

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Image-Based Ciphering of Video Streams and Object Recognition for Urban and Vehicular Surveillance Services

Cited by 2 publications

References 10 publications

Sensing Image Regions for Enhancing Accuracy in People Re-identification

Sensing Image Regions for Enhancing Accuracy in People Re-identification

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

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