Evolution of the Viola-Jones Object Detection Method: A Survey

Arlazarov, Vladimir V.; Voysyat, Ju.S.; Matalov, Daniil P.; Nikolaev, Dmitry P.; Usilin, Sergey A.

doi:10.14529/mmp210401

Cited by 5 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it should be emphasized that there have been improvements proposed for the VJ detector in the literature. In [39], the history of its development and modifications is reported.…”

Section: Viola-jones (Vj)mentioning

confidence: 99%

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection

Santos,

Montalvão,

Araujo

et al. 2024

Sensors

View full text Add to dashboard Cite

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola–Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ’s scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms’ strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.

show abstract

“…However, it should be emphasized that there have been improvements proposed for the VJ detector in the literature. In [39], the history of its development and modifications is reported.…”

Section: Viola-jones (Vj)mentioning

confidence: 99%

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection

Santos,

Montalvão,

Araujo

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…An image of an empty or filled checkbox is not difficult to detect using contour selection [12], CNN [13], or Viola and Jones method [15]. Let us assume that at the first stage of document processing, along with word and character recognition, checkbox frames were detected.…”

Section: Fig 2 Examples Of Checkboxesmentioning

confidence: 99%

Method for Analyzing the Structure of Noisy Images of Administrative Documents

Slavin,

Pliskin

2023

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

The problem of extracting content elements (fields) from the images of administrative documents via descriptions of anchoring elements is considered. Administrative documents contain static elements and content elements (filled information). The static objects of the document model are the lines of the document structure and the words. Sets of objects united by properties and relationships are described. The text descriptor can contain attributes that distinguish it from similar descriptors. We suggest using combined descriptors of line segments and words. We showed experimentally that the extraction of object sets improves the recognition accuracy of the document fields by 17% and the accuracy of information extraction by 16%. For optical character recognition, we employed SDK Smart Document Engine in the experiment.

show abstract