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REVIEW 4 major objections 6 minor 26 references

Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A driver-monitoring pipeline that flags when the camera cannot see the driver's face, keeping gaze and identification honest.

desk verdict Useful integration paper with a load-bearing occlusion-evaluation mismatch: crop-trained model deployed on full frames, plus unspecified occlusion labels. read the letter →

arxiv 2504.20677 v1 pith:JOU6KU52 submitted 2025-04-29 cs.CV

Paola Natalia Cañas , Alexander Diez , David Galvañ , Marcos Nieto , Igor Rodríguez This is my paper · ORCID
classification cs.CV
keywords drivermonitoringsystemocclusiondetectiongazeestimationbyregionsidentificationRGBandinfraredimagingdeeplearninglow-lightdriving
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a driver monitoring system can be made reliable under occlusion by adding a dedicated face-occlusion classifier that gates the other tasks. The proposed pipeline performs driver identification, gaze-region estimation, and occlusion detection on RGB and infrared images, switching to the IR channel when RGB face detection fails and warning the driver when no channel can see the face. The headline numbers are 86.29 percent RGB accuracy on nine gaze regions, 99.43 percent recall for occluded faces after face cropping, and 99.38 percent identification accuracy with a 0.81 percent false-accept rate. The authors argue that image-based occlusion detection is new for driver monitoring and that it matters because a system that knows when it cannot see is safer than one that guesses. If these results transfer to the car, an in-vehicle system could keep monitoring through darkness, glare, and partial face obstruction.

What carries the argument

The mechanism carrying the argument is an RGB-first pipeline with an occlusion-gated fallback to infrared. In each frame, an MTCNN face detector processes the RGB image; if a face is found, the crop is sent to the gaze classifier and the identity embedder, while the IR frame is held in reserve. If no face is found, a binary occlusion classifier decides whether an obstruction is present; only when no occlusion is detected may the system reuse the previous frame's bounding box, expanded by 20 percent. Persistent occlusion or repeated face-detection failure triggers a switch to the IR pipeline, and persistent occlusion there produces a driver alert and stops gaze and identification. The individual algorithms are EfficientNet-b0 for gaze regions, a MobileNet-v3-based binary classifier for occlusion, and FaceNet embeddings for identity.

What would settle it

Take a random sample of distraction-annotated frames from the public multi-modal driver monitoring dataset and have independent annotators decide whether each 'occlusion' label shows a visible face obstruction. If many labels correspond to hand-on-wheel or other distraction states, the reported 99.43 percent occlusion recall is not a measure of the claimed capability. Alternatively, run the RGB occlusion classifier on a controlled set of partially occluded faces; if recall drops sharply when the face is only partly covered, the practical robustness is narrower than the headline figure.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that a modular, calibration-free pipeline can combine gaze classification, driver identification, and occlusion detection, and that occlusion detection is what holds the other two together. The RGB gaze classifier uses an EfficientNet-b0 backbone on MTCNN-cropped faces and reaches 86.29 percent test accuracy across nine gaze regions; the RGB occlusion classifier reaches 99.43 percent recall on the occlusion class when given cropped faces, and the IR version reaches 90.24 percent; the identification module uses FaceNet embeddings with a cosine threshold and reports 99.38 percent accuracy on fifteen registered and ten unregistered individuals. The authors state that the RGB models consistently outperform the IR models, which they attribute to the lack of pretrained infrared models and to sensor-dependent IR image quality. They further claim this is the first academic attempt at image-based occlusion detection for driver monitoring, with the purpose of alerting the driver when the system cannot function correctly.

Load-bearing premise

The load-bearing premise is that the dataset's 'occlusion' labels actually mark visual obstructions of the face, so the high recall figures measure occlusion detection rather than some other driving state.

Editorial extensions

If this is right

  • A system that detects occlusion before running gaze or identification can alert the driver that monitoring is temporarily unavailable, rather than silently returning unreliable outputs.
  • The permanent RGB-to-IR switch after repeated face-detection failure gives a graceful degrade path for tunnels, nighttime, and direct sunlight glare.
  • Face-cropped preprocessing should be adopted in production because it raised occlusion recall from 95.34 percent to 99.43 percent and gaze accuracy from 79.12 percent to 86.29 percent.
  • The RGB-over-IR accuracy gap predicts that future gains will come from pretraining on infrared data or improving IR sensor image quality, not from architecture changes alone.
  • Because the pipeline is modular, adding distraction or drowsiness modules later should require only training a new head on the same image stream.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the occlusion training images came from a distraction-annotated partition, the same data could support a joint label that distinguishes 'driver not looking' from 'camera cannot see'; the current binary occlusion output leaves that distinction implicit.
  • A natural controlled experiment would be to run the pipeline with the RGB camera artificially covered and the IR channel dimmed, measuring how often gaze or identity still returns a wrong answer instead of an occlusion alert.
  • A temporal filter over consecutive occlusion predictions would likely remove the false alarms the authors observed when a hand approaches the face in IR; the paper's per-frame design does not exploit this.
  • If face cropping is what lifts occlusion recall from 95.34 percent to 99.43 percent, then improving the face detector's bounding-box accuracy could further improve occlusion detection without new occlusion labels.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents an occlusion-aware driver monitoring system (DMS) built on the Driver Monitoring Dataset (DMD). The system combines three components: gaze estimation into nine regions, driver identification via face embeddings, and face occlusion detection, each implemented for both RGB and IR images. The authors report test accuracies of 86.29% for RGB gaze classification, 76.76% for IR gaze classification, 99.43% occlusion recall for the RGB crop-trained model, 90.24% for the IR occlusion model, and 99.38% identification accuracy with low FAR/FRR. The pipeline prioritizes RGB processing, falls back to IR in low-light conditions, and invokes the occlusion detector when no face is detected, alerting the driver if occlusion persists. The paper claims to be the first academic effort to introduce occlusion detection from images for driver monitoring, aligned with EuroNCAP recommendations.

Significance. If the occlusion detection functionality works as claimed, it addresses a genuine safety gap in DMS: alerting when the system cannot see the driver, as recommended by EuroNCAP. The paper's strengths include use of a public dataset, evaluation of multiple architectures, explicit reporting of recall (which is appropriate for imbalanced occlusion data), and a described end-to-end pipeline with both RGB and IR branches. The quantitative results on the DMD are plausible, and the subject-disjoint train/test splits for gaze data reduce the risk of identity leakage. However, the central claim is undermined by two methodological gaps: the occlusion label definition is never specified, and the best-performing RGB occlusion model is trained on face crops but deployed on full frames in the no-face branch. These issues are fixable but require additional work, so the paper is not ready for acceptance in its current form.

major comments (4)
  1. [Section II.B] The occlusion labels are never defined. The text states that images were taken from the distraction-annotated part of the DMD because it contained 'a higher number of occluded images,' but it does not specify how occlusion was labeled, who labeled it, what annotation protocol was followed, or whether the labels correspond to visual obstructions of the face/eyes versus other distraction states such as hands on the wheel or looking away. Since the central claim of the paper is that occlusion detection ensures proper DMS functioning, the semantics of the occlusion label are load-bearing. If the labels encode a different concept, the reported 99.43% recall does not measure the claimed capability. Please describe the DMD occlusion annotation protocol, provide examples of positive and negative images, and ideally report a small human-verification study of the labels.
  2. [Section IV and Section VI] There is a train/deploy distribution mismatch for the RGB occlusion detector. The model with 99.43% occlusion recall was trained on MTCNN-cropped face images, but the pipeline described in Section VI sends the full RGB frame to the occlusion detector exactly when no face is detected. A crop exists only when face detection succeeds, so the no-face branch at inference receives a different input distribution than the training data. The earlier full-frame RGB model had a lower recall of 95.34%, and the paper does not state that this full-frame model is the one used in the integrated no-face branch. The headline 99.43% recall therefore does not validate the integrated pipeline. Please either deploy the full-frame model in the no-face branch and report its performance, or retrain/evaluate the crop-trained model on full-frame no-face images, or provide evidence that the distribution shift is negligible.
  3. [Section III (IR gaze classifier)] The IR gaze classifier was retrained after removing images with mean brightness above 235 or below 20, and the reported 76.76% test accuracy is measured on this filtered test set. The paper acknowledges that the unfiltered model had 84.70% accuracy but poor real-world performance, and that the filtered model was chosen because it was 'more representative of real-world conditions.' However, this filtering is an ad hoc, post-hoc exclusion of extreme lighting cases, and no evaluation is reported on the excluded images. Since the paper claims the system functions in challenging low-light scenarios, please quantify how the deployed IR model behaves on the removed extreme-brightness images, or specify the conditions under which the pipeline can be expected to operate.
  4. [Section VI (real-life tests)] The real-life tests are described only qualitatively. Statements such as 'the RGB occlusion classifier made accurate predictions in all scenarios we tested' and 'the IR gaze classifier did not perform as well' are not supported by quantitative data. There is no mention of the number of subjects, number of trials per scenario, or any metric such as accuracy or recall. Please provide a tabulated evaluation of the real-life tests, or weaken the conclusive statements about real-world reliability.
minor comments (6)
  1. [Abstract and Section I] Hyphenation is inconsistent: 'occlusion -aware' and 'gaze estimation by regions' appear with stray spaces in the abstract and introduction.
  2. [Equation (1)] Equation (1) is malformed in the manuscript; the fractions 4/5 and 1/5 are not legible due to formatting. Please ensure the equation renders correctly.
  3. [Section IV] The phrase 'a three-layer classification layer (instead of one)' is unclear; please specify the exact architecture change.
  4. [Section III] The initial MobileNet_v3_small gaze model underfitted with 59.12% accuracy, but no training details (learning rate, epochs, batch size) are given for either the underfitting or successful models. Providing a table of hyperparameters would improve reproducibility.
  5. [References] Several references are incomplete or have formatting issues, e.g., reference [14] lacks a venue, and reference [6] has a typo in the page range ('4318-432').
  6. [Figures 8 and 9] The captions for Figures 8 and 9 could be more explicit about which model produced each prediction and what the color coding or region labels mean.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: an empirical pipeline evaluated on held-out public data; the self-cited DMD dataset is independent support, and the noted distribution/label issues are correctness concerns, not circularity.

full rationale

This is an empirical systems paper rather than a derivation chain. The gaze, occlusion, and identification modules are trained and evaluated on held-out splits of the public DMD. The only formula, Eq. (1), is a running-mean update for face embeddings and is not used to derive the headline metrics. The cosine thresholds (0.65/0.575), MTCNN confidence (0.97), and brightness cutoffs are tuned hyperparameters; the reported accuracies are test-set measurements, not predictions forced by those settings. The DMD citation [18] and the earlier distraction-analysis paper [19] are self-citations by some co-authors, but the dataset is a public, externally usable resource and the paper's use of it does not reduce the system's outputs to the citation. The strongest concerns are data-label provenance for occlusion (Section II.B does not define how the occlusion labels were created) and a deployment mismatch (Section VI sends full frames to an occlusion detector that Section IV trained on MTCNN crops); these are validity and evaluation gaps, not circularity, because no step in the paper's chain equates an output to an input by construction.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The system depends on the quality of DMD annotations, on hand-tuned thresholds, and on the transferability of pretrained models. The cosine similarity thresholds, the MTCNN confidence, the brightness exclusion cutoffs, and the bounding box expansion factor are all fitted to the evaluation setting. The omitted description of occlusion label generation is the largest unstated dependency.

free parameters (5)
  • Cosine similarity threshold for RGB re-identification = 0.65
    Chosen after experimentation to optimize accuracy, FAR, and FRR during driver ID evaluation (Section V).
  • Cosine similarity threshold for IR re-identification = 0.575
    Chosen after experimentation for IR embeddings (Section V).
  • MTCNN face detection confidence threshold = 0.97
    Increased from the default to eliminate false positives during driver ID evaluation (Section V).
  • IR image mean brightness exclusion thresholds = mean brightness >235 or <20 removed
    Post-hoc data filtering to remove extreme lighting cases from the IR training set (Section III).
  • Estimated bounding box expansion factor = 20%
    Hand-chosen expansion of the previous face bounding box for tracking when no face is detected (Section V).
assumptions (4)
  • domain assumption DMD gaze region annotations are correct and correspond to actual gaze direction
    The gaze classifier is trained and evaluated on these labels; Section II.A describes manual removal of mismatched annotations, implying dependence on annotation quality.
  • domain assumption Occlusion labels derived from DMD distraction annotations are valid proxies for face or eye occlusion
    Section II.B selects images from the distraction-annotated part but does not define the occlusion labeling process.
  • standard math Standard deep learning training converges to meaningful classifiers under the stated augmentations
    The paper relies on standard empirical training of CNNs; no formal proof is provided.
  • domain assumption FaceNet embeddings transfer to in-vehicle RGB and IR face images
    FaceNet is used without fine-tuning on in-cabin IR data; the paper applies it directly to both modalities (Section V).

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Cite this review

Pith. "Pith review of Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset." pith.science (2026). https://pith.science/paper/JOU6KU52

@misc{pith2026250420677,
  author       = {Pith},
  title        = {Pith review of: Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JOU6KU52}},
  note         = {Machine review of arXiv:2504.20677}
}
read the original abstract

This paper presents a robust, occlusion-aware driver monitoring system (DMS) utilizing the Driver Monitoring Dataset (DMD). The system performs driver identification, gaze estimation by regions, and face occlusion detection under varying lighting conditions, including challenging low-light scenarios. Aligned with EuroNCAP recommendations, the inclusion of occlusion detection enhances situational awareness and system trustworthiness by indicating when the system's performance may be degraded. The system employs separate algorithms trained on RGB and infrared (IR) images to ensure reliable functioning. We detail the development and integration of these algorithms into a cohesive pipeline, addressing the challenges of working with different sensors and real-car implementation. Evaluation on the DMD and in real-world scenarios demonstrates the effectiveness of the proposed system, highlighting the superior performance of RGB-based models and the pioneering contribution of robust occlusion detection in DMS.

Figures

Figures reproduced from arXiv: 2504.20677 by the authors.

Figure 8
Figure 8. Gaze classifier prediction of a subject looking at left mirror The RGB occlusion classifier made accurate predictions in all scenarios we tested, including occluding the face with a hand or notebook, scratching eyes, scratching the forehead, and more. The IR classifier performed well in some cases, but was much less robust to different forms of occlusion. It often predicted occlusion when a hand was near the face bu… view at source ↗

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Taxonomy and Definitions for Terms Related to Driving Automation Systems for On -Road Motor Vehicles,

    SAE International, "Taxonomy and Definitions for Terms Related to Driving Automation Systems for On -Road Motor Vehicles," J3016_202104, Apr. 2021

    work page 2021

  2. [2]

    Assessment Protocol – Safety Assist Safe Driving,

    Euro NCAP, "Assessment Protocol – Safety Assist Safe Driving," Version 10.4, European New Car Assessment Programme, 2024

    work page 2024

  3. [3]

    Data-Driven Estimation of Driver Attention Using Calibration -Free Eye Gaze and Scene Features,

    Z. Hu, C. Lv, P. Hang, C. Huang, and Y. Xing, "Data-Driven Estimation of Driver Attention Using Calibration -Free Eye Gaze and Scene Features," IEEE Transactions on Industrial Electronics, vol. 69, no. 2, pp. 1800-1808, 2022, doi: 10.1109/TIE.2021.3

    work page doi:10.1109/tie.2021.3 2022

  4. [4]

    How do drivers allocate visual attention to vulnerable road users when turning at urban intersections?

    J. Girgis, M. Powell, B. Donmez, J. Pratt, and P. Hess, "How do drivers allocate visual attention to vulnerable road users when turning at urban intersections?" Transportation Research Interdisciplinary Perspectives, vol. 19, 2023, Art. no. 100822

    work page 2023

  5. [5]

    Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models,

    A. Jain, H. S. Koppula, B. Raghavan, S. Soh, and A. Saxena, "Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models," in IEEE ICCV, 2015, pp. 3182-3190

    work page 2015

  6. [6]

    A Dual - Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring,

    L. Yang, K. Dong, A. J. Dmitruk, J. Brighton, and Y. Zhao, "A Dual - Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring," IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 10, pp. 4318-432, 2020

    work page 2020

  7. [7]

    Speak2Label: using domain knowledge for creating a large scale driver gaze zone estimation dataset,

    S. Ghosh, A. Dhall, G. Sharma, S. Gupta, and N. Sebe, "Speak2Label: using domain knowledge for creating a large scale driver gaze zone estimation dataset," in Proceedings IEEE/CVF Workshops, 2021

    work page 2021

  8. [8]

    Driver Visual Attention Estimation Using Head Pose and Eye Appearance Information,

    S. Jha, N. Al-Dhahir, and C. Busso, "Driver Visual Attention Estimation Using Head Pose and Eye Appearance Information," IEEE Open Journal of Intelligent Transportation Systems, vol. 4, pp. 216 -231, 2023, doi: 10.1109/OJITS.2023.3258184

    work page arXiv 2023

Show all 26 references
  1. [9]

    USID - Unsupervised Identification of the Driver for Vehicle Comfort Functions,

    V. Vučinić, L. Eidel, M. Tang, and E. Ax, "USID - Unsupervised Identification of the Driver for Vehicle Comfort Functions," in Human Interaction and Emerging Technologies (IHIET-AI 2024), 2024

    2024

  2. [10]

    A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User’s Driving Preference,

    P. Chen, J. Wu, and N. Li, "A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User’s Driving Preference," Computational Intelligence and Neuroscience, vol. 2023, Art. no. 8978398, 2023

    2023

  3. [11]

    Evolving Payment Security: A Facial Recognition -Based Credit Card Reader with A Multifunctional Cascade Neural Network,

    K. Dahiya, J. Goel, A. Kaushik, K. Rai, K. Jain, and A. Gambhir, "Evolving Payment Security: A Facial Recognition -Based Credit Card Reader with A Multifunctional Cascade Neural Network," in IEEE International Conference on Computing, Power and Communicatio n Technologies (IC2...

    2024

  4. [12]

    Facial Authentication System Design of Online Interactive Platform for Innovation and Entrepreneurship Courses for Mobile Platform Terminals,

    L. Liu, "Facial Authentication System Design of Online Interactive Platform for Innovation and Entrepreneurship Courses for Mobile Platform Terminals," in 3rd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, 2022

    2022

  5. [13]

    Safe and Sustainable Fleet Management with Data Analytics and Training,

    T. R. Gresham, "Safe and Sustainable Fleet Management with Data Analytics and Training," in Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, 2021, pp. 1-6

    2021

  6. [14]

    Visual recognition systems in a car passenger compartment with the focus on facial driver identification,

    A. Makrushin, "Visual recognition systems in a car passenger compartment with the focus on facial driver identification," 2014

    2014

  7. [15]

    DriverID: Driver Identity System Based on Voiceprint and Acoustic Sensing,

    K. Chan and C. Chao, "DriverID: Driver Identity System Based on Voiceprint and Acoustic Sensing," in IEEE International Conference on Consumer Electronics 2022, Taipei, 2022, pp. 45-46

    2022

  8. [16]

    Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors,

    L. Bonfati, J. Junior, H. Siqueira, and S. Stevan, "Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors," Sensors, vol. 23, 2022, Art. no. 263

    2022

  9. [17]

    Searching Central Difference Convolutional Networks for Face Anti-Spoofing,

    Z. Yu, "Searching Central Difference Convolutional Networks for Face Anti-Spoofing," in IEEE/CVF CVPR, Seattle, 2020, pp. 5294-5304

    2020

  10. [18]

    Dmd: A large -scale multi -modal driver monitoring dataset for attention and alertness analysis,

    J. D. Ortega, N. Kose, P. Cañas, M.-A. Chao, A. Unnervik, M. Nieto, O. Otaegui, and L. Salgado, "Dmd: A large -scale multi -modal driver monitoring dataset for attention and alertness analysis," in Computer Vision–ECCV Workshops Proceedings, Part IV 16, Springer, 2020

    2020

  11. [19]

    Detection of Distraction- related Actions on DMD: An Image and a Video -based Approach Comparison,

    P. Cañas, J. Ortega, M. Nieto, and O. Otaegui, "Detection of Distraction- related Actions on DMD: An Image and a Video -based Approach Comparison," in Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applicatio...

    2021

  12. [20]

    Searching for MobileNetV3,

    A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam, "Searching for MobileNetV3," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019

    2019

  13. [21]

    EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,

    M. Tan and Q. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in Proceedings of the 36th International Conference on Machine Learning, K. Chaudhuri and R. Salakhutdinov, Eds., 09-15 Jun 2019, pp. 6105-6114

    2019

  14. [22]

    Joint face detection and alignment using multitask cascaded convolutional networks,

    K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint face detection and alignment using multitask cascaded convolutional networks," IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499-1503, 2016

    2016

  15. [23]

    AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty,

    D. Hendrycks, N. Mu, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan, "AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty," in Proceedings of the International Conference on Learning Representations (ICLR), 2020

    2020

  16. [24]

    Trivialaugment: Tuning-free yet state-of-the- art data augmentation,

    S. G. Müller and F. Hutter, "Trivialaugment: Tuning-free yet state-of-the- art data augmentation," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 774-782

    2021

  17. [25]

    FaceNet: A unified embedding for face recognition and clustering,

    F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering," in 2015 IEEE CVPR , 2015, pp. 815-823

    2015

  18. [26]

    Contrast limited adaptive histogram equalization based enhancement for real time video system,

    G. Yadav, S. Maheshwari, and A. Agarwal, "Contrast limited adaptive histogram equalization based enhancement for real time video system," in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 2392-23

    2014

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Reviewed August 16, 2026 · model on record in the stance chip above.