Abstract
There is a plenty of very interesting problems in the field of computer vision, from the very basic image classification problem to 3d pose estimation problem. One among the many interesting problems is object detection, which is the computer capability to accurately identify the multiple objects present in the scene (image or video) with the bounding boxes around them and the appropriate labels indicating their class along with the confidence score indicating the degree of closeness with the class. In this work, we have discussed in detail different types of region-based object detection models applied on both live video stream and images.
Keywords
- Object detection
- Deep learning
- Convolutional neural network
- Computer vision
- Single-shot detector
- You look only once
- Selective search
- Region of interest
- Region proposal network
This is a preview of subscription content, access via your institution.
Buying options







References
Huang J, Wang D, Wang X (2017) Novel single stage detectors for object detection
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp 21–37
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
Ren S, He K, Girshick R, Sun J. (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. arXiv preprint arXiv:1703.06870
Huang J et al (2017) Speed/accuracy trade-offs for modern convolutional object detectors. IEEE CVPR
Acknowledgements
We are thankful to NMAM Institute of Technology and Engineering Nitte, for allowing us to take the images required for performing the experiment.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shetty, J., Jogi, P.S. (2019). Study on Different Region-Based Object Detection Models Applied to Live Video Stream and Images Using Deep Learning. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)