Grocery Product Recognition to Aid Visually Impaired People
Resumo
This paper proposes a new approach in object recognition to assist visually impaired people. This approach achieved accuracy rates higher than the approaches proposed by the authors of the selected datasets. We applied Data Augmentation with other techniques and adjustments to different Pre-trained CNNs (Convolutional Neural Networks). The ResNet-50 based approach achieved the best results in the most recent datasets. This work focused on products that are usually found on grocery store shelves, supermarkets, refrigerators or pantries.
Palavras-chave:
Grocery Product Recognition, Object Recognition, Computer Vision
Referências
WHO, “Blindness and vision impairment,” 2021, [online] Disponível em: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment [Acesso em: Ago. 2021].
IBGE, “Releitura dos dados de pessoas com deficiência no censo demográfico 2010 à luz das recomendações do grupo de washington,” IBGE, RJ, Nota tecnica 01/2018, 2018.
D. Bal, M. M. Islam Tusher, M. Rahman, and M. S. Rahman Saymon, “Navix: A wearable navigation system for visually impaired persons,” in 2nd STI, 2020, pp. 1–4.
G. Vaidya, K. Vaidya, and K. Bhosale, “Text recognition system for visually impaired using portable camera,” in 2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW), 2020, pp. 1–4.
L. P. Sousa, R. M. S. Veras, L. H. S. Vogado, L. S. Britto Neto, R. R. V. Silva, F. H. D. Araujo, and F. N. S. Medeiros, “Banknote identification methodology for visually impaired people,” in 2020 IWSSIP, 2020, pp. 261–266.
L. Britto Neto, V. R. M. L. Maike, F. L. Koch, M. C. C. Baranauskas, A. Rocha, and S. Goldenstein, “A wearable face recognition system built into a smartwatch and the visually impaired user,” in ICEIS, INSTICC. SciTePress, 2015, pp. 5–12.
L. Britto Neto, F. Grijalva, V. R. M. L. Maike, L. C. Martini, D. Florencio, M. C. C. Baranauskas, A. Rocha, and S. Goldenstein, “A kinectbased wearable face recognition system to aid visually impaired users,” IEEE THMS, vol. 47, no. 1, pp. 52–64, 2017.
A. Machado, R. Veras, K. Aires, and L. Britto Neto, “A systematic review on product recognition for aiding visually impaired people,” IEEE LATAMT, vol. 19, no. 4, pp. 592–603, 2021.
J. Rivera-Rubio, S. Idrees, I. Alexiou, L. Hadjilucas, and A. A. Bharath, “Small hand-held object recognition test (short),” in IEEE WACV, 2014, pp. 524–531.
G. Varol and R. S. Kuzu, “Toward retail product recognition on grocery shelves,” in ICGIP 2014, Y. Wang, X. Jiang, and D. Zhang, Eds., vol. 9443, International Society for Optics and Photonics. SPIE, 2015, pp. 46 – 52.
P. Jund, N. Abdo, A. Eitel, and W. Burgard, “The freiburg groceries dataset,” 2016.
M. Klasson, C. Zhang, and H. Kjellström, “A hierarchical grocery store image dataset with visual and semantic labels,” in IEEE WACV, 2019, pp. 491–500.
M. Merler, C. Galleguillos, and S. Belongie, “Recognizing groceries in situ using in vitro training data,” in IEEE CVPR, 2007, pp. 1–8.
IBGE, “Releitura dos dados de pessoas com deficiência no censo demográfico 2010 à luz das recomendações do grupo de washington,” IBGE, RJ, Nota tecnica 01/2018, 2018.
D. Bal, M. M. Islam Tusher, M. Rahman, and M. S. Rahman Saymon, “Navix: A wearable navigation system for visually impaired persons,” in 2nd STI, 2020, pp. 1–4.
G. Vaidya, K. Vaidya, and K. Bhosale, “Text recognition system for visually impaired using portable camera,” in 2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW), 2020, pp. 1–4.
L. P. Sousa, R. M. S. Veras, L. H. S. Vogado, L. S. Britto Neto, R. R. V. Silva, F. H. D. Araujo, and F. N. S. Medeiros, “Banknote identification methodology for visually impaired people,” in 2020 IWSSIP, 2020, pp. 261–266.
L. Britto Neto, V. R. M. L. Maike, F. L. Koch, M. C. C. Baranauskas, A. Rocha, and S. Goldenstein, “A wearable face recognition system built into a smartwatch and the visually impaired user,” in ICEIS, INSTICC. SciTePress, 2015, pp. 5–12.
L. Britto Neto, F. Grijalva, V. R. M. L. Maike, L. C. Martini, D. Florencio, M. C. C. Baranauskas, A. Rocha, and S. Goldenstein, “A kinectbased wearable face recognition system to aid visually impaired users,” IEEE THMS, vol. 47, no. 1, pp. 52–64, 2017.
A. Machado, R. Veras, K. Aires, and L. Britto Neto, “A systematic review on product recognition for aiding visually impaired people,” IEEE LATAMT, vol. 19, no. 4, pp. 592–603, 2021.
J. Rivera-Rubio, S. Idrees, I. Alexiou, L. Hadjilucas, and A. A. Bharath, “Small hand-held object recognition test (short),” in IEEE WACV, 2014, pp. 524–531.
G. Varol and R. S. Kuzu, “Toward retail product recognition on grocery shelves,” in ICGIP 2014, Y. Wang, X. Jiang, and D. Zhang, Eds., vol. 9443, International Society for Optics and Photonics. SPIE, 2015, pp. 46 – 52.
P. Jund, N. Abdo, A. Eitel, and W. Burgard, “The freiburg groceries dataset,” 2016.
M. Klasson, C. Zhang, and H. Kjellström, “A hierarchical grocery store image dataset with visual and semantic labels,” in IEEE WACV, 2019, pp. 491–500.
M. Merler, C. Galleguillos, and S. Belongie, “Recognizing groceries in situ using in vitro training data,” in IEEE CVPR, 2007, pp. 1–8.
Publicado
22/11/2021
Como Citar
MACHADO, André de Lima; AIRES, Kelson; VERAS, Rodrigo; BRITTO NETO, Laurindo.
Grocery Product Recognition to Aid Visually Impaired People. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 94-99.
DOI: https://doi.org/10.5753/wvc.2021.18896.
