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Things at Your Desk: A Portable Object Dataset

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Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1022))

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Abstract

Object Recognition has been a field in Computer Vision research, which is far from being solved when it comes to localizing the object of interest in an unconstrained environment, captured from different viewing angles. Lack of benchmark datasets clogs the progress in this field since the last decade, barring the subset of a single dataset, alias the Office dataset, which attempted to boost research in the field of pose-invariant detection and recognition of portable object in unconstrained environment. A new challenging object dataset with 30 categories has been proposed with a vision to boost the performances of the task of object recognition for portable objects, thus enhancing the study of cross domain adaptation, in conjunction to the Office dataset. Images of various hand-held objects are captured by the primary camera of a smartphone, where they are photographed under unconstrained environment with varied illumination conditions at different viewing angles. The monte-carlo object detection and recognition has been performed for the proposed dataset, facilitated by existing state-of-the-art transfer learning techniques for cross-domain recognition of objects. The baseline accuracies for existing Domain Adaptation methods, published recently, are also presented in this paper, for the kind perusal of the researchers. A new technique has also been proposed based on the activation maps of the AlexNet to detect objects, alongwith a Generative Adversarial Network (GAN) based Domain Adaptation technique for Object Recognition.

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Acknowledgements

We gratefully thank the faculty and researchers of Visualization and Perception Lab, IIT Madras, for their valuable insight into this research.

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Authors and Affiliations

  1. VP Lab, Department of Computer Science and Engineering, IIT Madras, Chennai, India

    Saptakatha Adak

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  1. Saptakatha Adak
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Correspondence to Saptakatha Adak .

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Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. Department of Advanced Information Technology and Computer Sciences, Tokyo Institute of Agriculture and Technology, Koganei, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Adak, S. (2020). Things at Your Desk: A Portable Object Dataset. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_36

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  • DOI: https://doi.org/10.1007/978-981-32-9088-4_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9087-7

  • Online ISBN: 978-981-32-9088-4

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