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Model-based recognition in robot vision for monitoring built environments

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Abstract

In contrast to the machine, people have strong perception to determine the multiple objects easily in various environments under certain conditions. It is evident that the objects may exist in different orientations, scales and/or shapes, however it does not affect our ability to correct object recognition accuracy. The computer vision based object recognition is one of the most tedious tasks. So, objective is to design and development of the robust object recognition system to recognize the multiple objects effectively since different variations in the natural images are existed. In this paper, authors have presented comparative analysis and studies of some state of art object-recognition models for robot vision based problems, where the main goal is to recognize position, orientations and the identity of objects/parts, where parts have been taken from the industry. In the dynamic environment problem, the parts are recognized in a complex scenario. This paper presented based on the object representations using the recognition algorithms. Three steps are discussed and examined in details which are common to each and every category, namely, feature extraction, modeling, and matching. Test results confirmed that proposed model-based recognition in robot vision is fast and provides 93.00% accuracy. The proposed algorithm for object-recognition model has been compared with existing industrial part-recognition models and has, provided insights for progress toward future robot vision systems.

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Acknowledgements

This work supported by the Department of Computer Application, Integral University, Lucknow, UP, India.

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We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

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Authors and Affiliations

  1. Department of Computer Applications, Integral University, Dasauli, Bas-ha Kursi Road, Lucknow, Uttar Pradesh, 226026, India

    Asif Khan

  2. Department of Computer Science and Engineering, GITAM (Deemed to be University), Hyderabad Campus, Hyderabad, Telangana, 502329, India

    Naushad Varish

  3. Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, Lucknow, India

    Dhirendra Pandey

  4. School of Computer Science, Guangzhou University (GU), Guangzhou, Guangdong, 510006, China

    Syed Qasim Afser Rizvi

  5. Department of Computer Science and Engineering, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, 244001, India

    Shashi Mehrotra

  6. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Gunur, Andhra Pradesh, 522302, India

    Nikhat Parveen

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  1. Asif Khan
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  2. Naushad Varish
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  4. Syed Qasim Afser Rizvi
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Contributions

Asif Khan: Conceptualization, Methodology, Software, Validation, Investigation, Data Creation, Writing - Original Draft. Naushad Varish: Writing - Review and Editing

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Correspondence to Naushad Varish.

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Khan, A., Varish, N., Pandey, D. et al. Model-based recognition in robot vision for monitoring built environments. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19323-4

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