Multicamera Human Re-Identification based on Covariance Descriptor

Devyatkov, Vladimir; Alfimtsev, Alexander; Taranyan, A. R.

doi:10.1134/s1054661818020025

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…figures. Some human detection algorithms are based on the subject's silhouette or edges [12], matching templates [13], sector rings [14], thermal images [15], visible human attributes [16], spatiotemporal features [17] [18], covariance descriptor [6], and feature maps derived from convolutional neural networks [2] [19]. Histogram of Oriented Gradients (HOG) is used for pedestrian detection [20], which performed relatively well for upright human figures.…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

“…Human detection has been used in many applications such as crime mitigation [1], surveillance [2] [3], crowd-estimate or people counting [4], abnormal event recognition [5], person reidentification [6], gender classification [7], elderly fall detection [8] [9], illegal trespassing detection [10], and rescue operations [11]. In applications of human detection where the subject video or images are captured using unmanned aerial vehicles (UAV), the difficulty of automatic human detection is amplified by the variations in pose, the angle, and the distance by which a camera is capturing an image or video sequence mounted in UAV.…”

Section: Introductionmentioning

confidence: 99%

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Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

Ocampo

Dadios

2020

Int. J. Adv. Intell. Informatics

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In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments.

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Section: A R T I C L E I N F Omentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

Ocampo

Dadios

2020

Int. J. Adv. Intell. Informatics

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“…The main idea of our project is the creation of a biometric security system based on the detection of persons in masks "Point of View" [19]. This technology will be presented in the form of a complex consisting of high-speed video surveillance cameras [22], software for Microsoft Windows [5], [17].…”

Section: Introductionmentioning

confidence: 99%

“…Those people whose faces are hidden behind the masking items will be indicated on the security monitor as a person with a heightened threat. At this stage, there are two scenarios: the control over the security of the facility will be fully placed on the shoulders of the security service, and the program will pay attention of the security service to people with difficult-to-recognize persons, after which the guards make a decision on each person [20]; the software independently makes an assumption about each person with a difficult to recognize face, and with the help of a trigger camera, which will be installed at the place of a teller or employee of the hall, monitors the behavior of each employee [5], [14]. This system is able to replace the emergency call button, which will shorten the response time of the security services and increase the degree of security of the object [17].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Video Surveillance Systems with the Purpose of Increasing the Economic Efficiency of Lending Financial Institution

Pisareva¹,

Chekunkov²,

Vakhnin³

2020

Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020)

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The essence of the project is the development of intelligent video surveillance systems and the implementation of sales of hardware and software security systems using face recognition technology. The payback period of the project is ten months. The total revenue for 2020 is planned to be 5, 75 million rubles. The project implementation period is from January 10, 2020, to December 31, 2020. The work calculated the material costs of the company LLC "Point of View" on the development of the project. A new algorithm for recognizing masked people is proposed. The main advantage of the proposed algorithm is that it uses a human mask, so it is applicable for video data with an arbitrary complex background. The results of testing the algorithm on its own sample of video sequences obtained from a stationary video surveillance camera are given.

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“…The information provided by a single camera is limited, so in order to monitor a wide area or to obtain more information from the different viewpoints of a region of interest, it is necessary to use more than one camera. For this reason, the use of several cameras is a common way of developing applications [3,4], since it is also useful for solving occlusions in scenarios with a high density of people/objects and for 3D applications [5,6]. The use of a multi-camera environment in scenarios with possible occlusions usually improves the detection performance with respect to the use of the cameras independently.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization †

Martín-Nieto

García-Martín

Martínez

et al. 2018

Sensors

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Finding optimal parametrizations for people detectors is a complicated task due to the large number of parameters and the high variability of application scenarios. In this paper, we propose a framework to adapt and improve any detector automatically in multi-camera scenarios where people are observed from various viewpoints. By accurately transferring detector results between camera viewpoints and by self-correlating these transferred results, the best configuration (in this paper, the detection threshold) for each detector-viewpoint pair is identified online without requiring any additional manually-labeled ground truth apart from the offline training of the detection model. Such a configuration consists of establishing the confidence detection threshold present in every people detector, which is a critical parameter affecting detection performance. The experimental results demonstrate that the proposed framework improves the performance of four different state-of-the-art detectors (DPM , ACF, faster R-CNN, and YOLO9000) whose Optimal Fixed Thresholds (OFTs) have been determined and fixed during training time using standard datasets.

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Multicamera Human Re-Identification based on Covariance Descriptor

Cited by 8 publications

References 21 publications

Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

Intelligent Video Surveillance Systems with the Purpose of Increasing the Economic Efficiency of Lending Financial Institution

Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization †

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