SpotGarbage: Smartphone app to detect garbage using deep learning

UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Volumn , Issue , 2016, Pages 940-945

  Mittal, Gaurav ^a   Yagnik, Kaushal B ^a   Garg, Mohit ^a   Krishnan, Narayanan C ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY ROPAR   (India)

Author keywords

Android; Computer vision; Deep learning; Fully convolutional neural networks; Garbage detection; Smartphone

Indexed keywords

COMPUTER VISION; CONSTRAINED OPTIMIZATION; CONVOLUTION; DEVELOPING COUNTRIES; NEURAL NETWORKS; SIGNAL ENCODING; SMARTPHONES; UBIQUITOUS COMPUTING;

ANDROID; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; DEEP ARCHITECTURES; DEEP LEARNING; MEMORY USAGE; PREDICTION TIME;

NETWORK ARCHITECTURE;

EID: 84991489204     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2971648.2971731     Document Type: Conference Paper

References (27)

1. Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari, "What is an object?", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010.
2. Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari. "Measuring the objectness of image windows", IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2189-2202, 2012.
3. Sean Bell, Paul Upchurch, Noah Snavely, and Kavita Bala, "Material recognition in the wild with the materials in context database." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3479-3487, 2015.
4. Yoshua Bengio, "Learning deep architectures for AI", Journal of Foundations and trendsR in Machine Learning, 2(1), 1-127, 2009.
5. Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, and Yixin Chen, "Compressing neural networks with the hashing trick", arXiv preprint arXiv:1504.04788, 2015.
6. Navneet Dalal, and Bill Triggs, "Histograms of oriented gradients for human detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 886-893, 2005.
7. Emily L Denton, WOjciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fegus, "Exploiting linear structure within convolutional networks for efficient evaluation", Advances in Neural Information Processing Systems, 2014.
8. Clement Farabet, C Couprie, L Najman, and Yann LeCun, "Learning hierarchical features for scene labeling", IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1915-1929, 2013.
9. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models", IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627-1645, 2010.
10. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep Residual Learning for Image Recognition", arXiv preprint arXiv:1512.03385, 2013.
11. Anil K. Jain, and Farshid Farrokhnia, "Unsupervised texture segmentation using Gabor filters", Proceedings of the IEEE Conference on Systems, Man and Cybernetics, 14-19, 1990.
12. Anil K. Jain, Nalini K. Ratha, and Sridhar Lakshmanan, "Object detection using Gabor filters, ", Pattern Recognition 30(2), 295-309, 1997.
13. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification with deep convolutional neural networks", Advances in Neural Information Processing Systems, 2012.
14. Gil Levi, and Tal Hassner, "Age and gender classification using convolutional neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 34-42, 2015.
15. Jonathan Long, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
16. David G. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, 60 (2) 91-110, 2004.
17. Bangalore S. Manjunath, and Wei-Ying Ma, "Texture features for browsing and retrieval of image data", IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 837-842, 1997.
18. Wanli Ouyang, et al., "Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection", Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2015.
19. Florent Perronnin, Jorge Saanchez, and Thomas Mensink, "Improving the fisher kernel for large-scale image classification", Proceedings of the European Conference on Computer Vision, 143-156, 2010.
20. Pedro H. O. Pinheiro, and Ronan Collober, "Recurrent convolutional neural networks for scene parsing", arXiv preprint arXiv:1306.2795, 2013.
21. Mahek Shah, "Swachh Bharat - Clean India Android App", at https://goo.gl/BG5KOJ
22. Karen Simonyan, and Andrew Zisserman, "Very deep convolutional networks for large-scale image recognition", Proceedings of the International Conference on Learning Representations, 2015.
23. Christian Szegedy, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
24. Jasper R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, "Selective search for object recognition", International Journal of computer vision, 104(2), 154-171, 2013.
25. "caffe-android-lib", at https://github.com/sh1r0/caffe-android-lib
26. "Swachchh Delhi App fails to meet expectations", at http://goo.gl/VqYDKP
27. "Frugal innovation and innovation for the frugal" at http://goo.gl/c7MX6k