YOLO-face: a real-time face detector

Chen, Weijun; Huang, Hongbo; Peng, Shuping; Zhou, Changsheng; Zhang, Cuiping

doi:10.1007/s00371-020-01831-7

Cited by 192 publications

(84 citation statements)

References 22 publications

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“…In comparison to the audio-only system, the video tracker in the second row in Tab. 1 unsurprisingly achieves a significant higher FR of 83.45%, due to its access to a highly informative modality, in line with its good benchmark results in other works [20,26]. Note that no threshold is needed for the video tracker, as its output is binary.…”

Section: Baseline Methodssupporting

confidence: 76%

Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain

Wissing

Boenninghoff

Kolossa

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization. Both applications benefit from a known speaker position when, for instance, applying beamforming or assigning unique speaker identities. Recently, several approaches utilizing acoustic signals augmented with visual data have been proposed for this task. However, both the acoustic and the visual modality may be corrupted in specific spatial regions, for instance due to poor lighting conditions or to the presence of background noise. This paper proposes a novel audiovisual data fusion framework for speaker localization by assigning individual dynamic stream weights to specific regions in the localization space. This fusion is achieved via a neural network, which combines the predictions of individual audio and video trackers based on their time-and location-dependent reliability. A performance evaluation using audiovisual recordings yields promising results, with the proposed fusion approach outperforming all baseline models.

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Section: Baseline Methodssupporting

confidence: 76%

Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain

Wissing

Boenninghoff

Kolossa

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…Detecting a license plate is a challenging task in the wild due to the changing weather conditions and lightning. Despite their high performance in other object detection tasks such as face recognition [58], [59], the direct use of YOLO detectors exhibit comparatively low performance in license plate detection. Hence, they have customized the YOLO and YOLO-2 models by modifying their grid sizes and bounding box parameters.…”

Section: E Statistical Classifiersmentioning

confidence: 99%

Automated License Plate Recognition: A Survey on Methods and Techniques

et al. 2021

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“…Furthermore, Figure 7 shows the accuracy of different YOLO algorithms in the case of easy, medium, and hard validation datasets. This figure shows that the proposed YOLOv3 has high accuracy compared with the YOLOv2 in the case of different validation datasets [ 33 ]. Furthermore, the proposed YOLOv3 is tested with a sample photo to confirm the capability of the model to detect a large number of persons (16 persons) before the real-time implementation.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi

Tran

Mahmoud

et al. 2021

Sensors

137

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Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

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YOLO-face: a real-time face detector

Cited by 192 publications

References 22 publications

Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain

Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain

Automated License Plate Recognition: A Survey on Methods and Techniques

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

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