A Hybrid CNN-Multiclass SVM Model for Household Object Recognition: A System for Domestic Robotic Vision
DOI:
https://doi.org/10.47839/ijc.24.2.4020Keywords:
Deep Learning, Convolution Neural Network, Tensorflow, Object Recognition, RGB-D datasetAbstract
Computer vision is a broad area covering many aspects of detection and recognition of objects. Household object recognition is a challenging problem, mainly due to lighting conditions, differences in shape, size of objects, position, occlusion, clutter of the objects and background. A novel deep learning and multiclass Support Vector Machine (SVM) based mechanism for household object recognition, is presented here. A comprehensive dataset of household objects, Washington RGB-D, is used. The Convolutional Neural Network (CNN) is used for feature extraction. It involves building and training CNN model using RGB-D dataset. Once CNN model is trained the features are automatically extracted from fully connected layer. The normalized feature vectors are used as input to train the SVM classifier. Empirical analysis of the proposed system's performance is carried out using RGB-D and real-time datasets. This hybrid model classifies the real-time instances with an accuracy of 89%. It exhibited an accuracy of 90.2% with RGB-D standard dataset, taken alone. The robustness and accuracy of the proposed approach pave the way for improved interaction between robots and humans in interior environments.
References
D.A.Bhayana, O.P.Verma, “Analysis of existing datasets of household objects for AI-enabled techniques,” In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, 2023, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-99-2100-3_4.
E. Arulprakash, M. Aruldoss, “A study on generic object detection with emphasis on future research directions,”Journal of King Saud University - Computer and Information Sciences, vol. 34, issue 9, pp. 7347-7365, 2022.https://doi.org/10.1016/j.jksuci.2021.08.001.
D. Raj, “Recent object detection techniques: a survey,” International Journal of Image, Graphics and Signal Processing, vol. 13, no. 2, 47, 2022.https://doi.org/10.5815/ijigsp.2022.02.05.
S. Tyreeet al., “6-DoF pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark,” Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 13081-13088, https://doi.org/10.1109/IROS47612.2022.9981838.
M. Attamimi, D. Purwanto and R. Dikairono, “Integration of color and shape features for household object recognition,” Proceedings of the2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Semarang, Indonesia, 2021, pp. 169-174, https://doi.org/10.23919/EECSI53397.2021.96242543919/EECSI53397.2021.9624254.
R. B.Wang, D. K.Yan, L.Xu, “A brief survey on recent advances of object detection with deep learning,” In: Pan, JS., Li, J., Namsrai, OE., Meng, Z., Savić, M. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 211, 2021. Springer, Singapore. https://doi.org/10.1007/978-981-33-6420-2_43.
L. E. Van Dyck, R. Kwitt, S. J. Denzler and W. R. Gruber, “Comparing object recognition in humans and deep convolutional neural networks – An eye tracking study,”Front. Neurosci.,vol. 15, 750639, 2021. https://doi.org/10.3389/fnins.2021.750639.
S.Gour, P.B.Patil, B.S.Malapur, “Multi-class support vector machine-based household object recognition system using features supported by point cloud library,” In: Sharma, H., Saraswat, M., Kumar, S., Bansal, J.C. (eds) Intelligent Learning for Computer Vision. CIS 2020. Lecture Notes on Data Engineering and Communications Technologies, 2021, vol 61. Springer, Singapore. https://doi.org/10.1007/978-981-33-4582-9_8.
R. Bagwe, R. Natharani, K. George and A. Panangadan, “Natural language controlled real-time object recognition framework for household robot,”Proceedings of the2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), NV, USA, 2021, pp. 1215-1220, https://doi.org/10.1109/CCWC51732.2021.9376044.
M. Attamimi, K. Liusiani, A. N. Irfansyah, D. Purwanto and R. Dikairono, “Development of visual data acquisition systems of household objects,”Proceedings of the2021 International Electronics Symposium (IES), Surabaya, Indonesia, 2021, pp. 411-416, https://doi.org/10.1109/IES53407.2021.9594002.
Y.Xiao, Z.Tian, J.Yu, et al., “A review of object detection based on deep learning,” Multimedia Tools and Applications, pp. 23729–23791, 2020. https://doi.org/10.1007/s11042-020-08976-6.
M. Abbas, J. Narayan, S. Banerjee and S. K. Dwivedy, “AlexNet based real-time detection and segregation of household objects using scorbot,”Proceedings of the2020 4th International Conference on Computational Intelligence and Networks (CINE), Kolkata, India, 2020, pp. 1-6, https://doi.org/10.1109/CINE48825.2020.234392.
W. Chen, W. Chen, C. He, N. Liu, P. Wu and H. Shi, “Research on classification and detection system of common household tools for home service robot,”Proceedings of the2020 International Conference
on System Science and Engineering (ICSSE), Kagawa, Japan, 2020, pp. 1-5, https://doi.org/10.1109/ICSSE50014.2020.9219314.
S. Gour, and P. B. Patil, “An exploration of deep learning in recognizing household objects,” Grenze International Journal of Engineering & Technology (GIJET), no. 6, 2020.
G. Wilson, C. Pereyda, et. all, “Robot-enabled support of daily activities in smart home environments,”Cognitive Systems Research, vol. 54, pp. 258-272, 2019, https://doi.org/10.1016/j.cogsys.2018.10.032.
Md G. Sarowar, Md. A. Razzak, and Md. A. Al Fuad, “HOG feature descriptor based PCA with SVM for efficient & accurate classification of objects in image,”Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing (IACC), 2019.https://doi.org/10.1109/IACC48062.2019.8971585.
N. Surantha, W. R. Wicaksono, “Design of smart home security system using object recognition and PIR sensor,”Procedia Computer Science, vol. 135, pp. 465-472, 2018, https://doi.org/10.1016/j.procs.2018.08.198.
A. Sarkale, et al., “An innovative machine learning approach for object detection and recognition,”Proceedings of the 2018 Second IEEE International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018. https://doi.org/10.1109/ICICCT.2018.8473221.
S. Gour, and P. B. Patil, “A novel machine learning approach to recognize household objects,”Proceedings of the 2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016.https://doi.org/10.1109/SCOPES.2016.7955543.
K. Alhamzi, M. Elmogy, S. Barakat, “3D object recognition based on local and global features using point cloud library,” International Journal of Advancements in Computing Technology (IJACT), vol. 7, no. 3, pp.43-54, 2015.
K.Lai, L.Bo, X.Ren, D.Fox, “A large-scale hierarchical multi-view RGB-D object dataset,”Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China, 9–13 May 2011, pp. 1817–1824.https://doi.org/10.1109/ICRA.2011.5980382.
R.Girshick, J.Donahue, T.Darrell, J.Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,”Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014, pp. 580–587.https://doi.org/10.1109/CVPR.2014.81.
L.Bo, X.Ren, D.Fox, “Depth kernel descriptors for object recognition,”Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, 25–30 September 2011, pp. 821–826.https://doi.org/10.1109/IROS.2011.6095119.
R.Socher, B.Huval, B.Bhat, C.D.Manning, A.Y.Ng, “Convolutional-recursive deep learning for 3D object classification,”Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–6 December 2012, pp. 656–664.
L.Bo, X.Ren, D.Fox, “Unsupervised feature learning for RGB-D based object recognition,”Proceedings of the International Symposium on Experimental Robotics, Québec City, QC, Canada, 18-21 June 2012, pp. 387–402.https://doi.org/10.1007/978-3-319-00065-7_27.
Y. Zhang, M. Yin, H. Wang and C. Hua, “Cross-level multi-modal features learning with transformer for RGB-D object recognition,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 12, pp. 7121-7130, 2023, https://doi.org/10.1109/TCSVT.2023.3275814.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
