Demystified the Function of Deep Mastering Strategies Based On CNN for Object Detection

Abstract

The extensive applications of object detection in robotics, self-riding automobiles, scene understanding, video surveillance etc triggered huge studies in the area of computer vision. Being in the middle of these applications, visual recognition structures which include image classification, localization and detection have a high priority these days. Due to the remarkable up gradation in neural networks particularly in deep learning, the visual recognition structures have attained an exceptional performance. Object detection is such a domain witnessing equisite achievement in computer vision. Here this paper ymbolizes the function of deep learning techniques primarily based totally on convolutional neural network for the object detection. Deep learning strategies for modern day object detection are assessed in this paper. 

Country : India

1 Suneeta Netala

  1. Associate Professor, Department of Computer Science and Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India

IRJIET, Volume 2, Issue 1, March 2018 pp. 53-56

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References

  1. Felzenszwalb, Pedro F, Ross B Girshick, David McAllester, and Deva Ramanan. (2010). “Object Detection with Discriminatively Trained Part-Based Models.” IEEE transactions on pattern analysis and machine intelligence,32(9): 1627–45.
  2. Ouyang, W et al. (2015). “DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2403–2412.
  3. Huang, Chen, Zhihai He, Guitao Cao, and Wenming Cao. (2016). “Task-Driven Progressive Part Localization for FineGrained Object Recognition.” IEEE Transactions on Multimedia, 18(12): 2372–83.
  4. Huang, Chen, Zhihai He, Guitao Cao, and Wenming Cao. (2016). “Task-Driven Progressive Part Localization for FineGrained Object Recognition.” IEEE Transactions on Multimedia, 18(12): 2372–83.
  5. Ohn-Bar, Eshed, and Mohan Manubhai Trivedi. (2017). “Multi-Scale Volumes for Deep Object Detection and Localization.” Pattern Recognition, 61: 557–72.
  6. Chang, Wo L. (2015). NIST Big Data Interoperability Framework: Volume 3, Use Cases and General Requirements. [13] Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. (2013). “Deep Neural Networks for Object Detection.” In Advances in Neural Information Processing Systems, 2553–61.
  7. Erhan, Dumitru, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov. (2014). “Scalable Object Detection Using Deep Neural Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2147–54.
  8. Zeiler, Matthew D, and Rob Fergus. (2014). “Visualizing and Understanding Convolutional Networks.” In European Conference on Computer Vision, 818–33.
  9. Sermanet, Pierre et al. (2013). “Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks.” arXiv preprint arXiv:1312.6229.
  10. Girshick, Ross, Jeff Donahue, Trevor Darrell, and Jitendra Malik. (2014). “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580–87.
  11. Wang, Xiaoyu, Ming Yang, Shenghuo Zhu, and Yuanqing Lin. (2015). “Regionlets for Generic Object Detection.” IEEE transactions on pattern analysis and machine intelligence, 37(10): 2071–84.