Face tracking with convolutional neural network heat-map

Do, Nhu-Tai; Kim, Soo-Hyung; Yang, Hyung-Jeong; Lee, Guee-Sang; Na, In Seop

doi:10.1145/3184066.3184081

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…A 3.5% improvement over ANN model was achieved. This shows that the ANN model's prediction is quite valid considering that ResNet-50 has a fully connected 1000 layers (fc1000) before the classification layer based on the architecture of CNN which is expected to ordinarily outperform a classical NN containing only thirty-five (35) hidden layers before the output layer.…”

Section: Resultsmentioning

confidence: 87%

“…In like manner, Hu et al in [34] reported that though Time Series images with the potential to describe periodical features of agricultural crops due to their great progressive tenacity do not essentially generate better classification accuracy. Other application of CNN can be seen in [35] as reported by Do et al as it is used for face and non-face classification.…”

Section: Review Of Related Workmentioning

confidence: 99%

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Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.

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Section: Resultsmentioning

confidence: 87%

Section: Review Of Related Workmentioning

confidence: 99%

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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“…ANN outperformed SVM in their results Hu et al (2016). stated that though Time series can explain seasonal characteristics of agricultural crops, they are not good in classification tasks Do, Kim, Yang, Lee, & Na (2018). reported facial identification and non-facial identification and classification task by CNN.…”

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confidence: 99%

Determining the Optimum Maturity of Maize Using Googlenet

Peter¹

2021

FJS

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Climatic changes, animal and human activities that lead to desertification and deforestation have affected the increase in agricultural produce especially in sub-Sahara Africa. Several efforts have been put in place to reduce these effects. However, that has not fully resolved the problem food shortages due to the growing population in sub-Sahara Africa. The application of image processing and convolutional neural network in the determination of the optimum maturity of SAMMAZ 17 variety of maize plant is being considered to mitigate for the shortage of food production. The optimum maturity is determined by using GoogleNet pre trained network on 3000 samples of maize comb captured using a camera at different maturity stages in a farmland. GoogleNet pre-trained network gave an accuracy of 82.44%. The result obtained showed a 10.44% improvement over an earlier result using Alexnet pre-trained network. The results suggest that when made operational there is a window of opportunity for increase in the production of food in sub-Sahara Africa

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“…Dari artikel yang membahas mengenai Non-Max Suppression (NMS), teknik ini mampu menghilangkan beberapa kandidat dengan mengambil nilai maksimum dari masing-masing kandidat area yang muncul [18]. Teknik lain seperti heat maps juga mampu menghasilkan hanya satu kandidat area di setiap objek [19], [20]. Pada penelitian deteksi objek yang menggunakan teknik heat maps, teknik ini mampu memberikan lokalisasi secara optimum dan memberikan informasi semantik dalam algoritme penelusuran [19].…”

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“…Teknik lain seperti heat maps juga mampu menghasilkan hanya satu kandidat area di setiap objek [19], [20]. Pada penelitian deteksi objek yang menggunakan teknik heat maps, teknik ini mampu memberikan lokalisasi secara optimum dan memberikan informasi semantik dalam algoritme penelusuran [19].…”

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Identifikasi Motif Jepara pada Ukiran dengan Memanfaatkan Convolutional Neural Network

Sandhopi¹,

P.C.S.W²,

Kristian³

2020

JNTETI

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Semakin berkembang motif ukiran, semakin beragam bentuk dan variasinya. Hal ini menyulitkan dalam menentukan suatu ukiran bermotif Jepara. Pada makalah ini, metode transfer learning dengan FC yang dikembangkan dimanfaatkan untuk mengidentifikasi motif khas Jepara pada suatu ukiran. Dataset dibedakan menjadi tiga color space, yaitu LUV, RGB, dan YcrCb. Selain itu, sliding window, non-max suppression, dan heat maps dimanfaatkan untuk proses penelusuran area objek ukiran dan pengidentifikasian motif Jepara. Hasil pengujian dari semua bobot menunjukkan bahwa Xception pada klasifikasi motif Jepara memiliki nilai akurasi tertinggi, yaitu 0,95, 0,95, dan 0,94 untuk masing-masing dataset color space LUV, RGB, dan YCrCb. Namun, ketika semua bobot model tersebut diterapkan pada sistem identifikasi motif Jepara, ResNet50 mampu mengungguli semua jaringan dengan nilai persentase identifikasi motif sebesar 84%, 79%, dan 80%, untuk masing-masing color space LUV, RGB, dan YCrCb. Hasil ini membuktikan bahwa sistem mampu membantu dalam proses menentukan suatu ukiran, termasuk ke dalam ukiran Jepara atau bukan, dengan mengidentifikasi motif-motif khas Jepara yang terdapat dalam ukiran.

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Face tracking with convolutional neural network heat-map

Cited by 10 publications

References 15 publications

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Determining the Optimum Maturity of Maize Using Googlenet

Identifikasi Motif Jepara pada Ukiran dengan Memanfaatkan Convolutional Neural Network

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