A Comparative Study of Spatial Hierarchies and Pose-Aware Object Detection

References

[1] Achlioptas, P., Diamanti, O., Mitliagkas, I., & Guibas, L. (2017). Representation Learning and Adversarial Generation of 3D Point Clouds. 35th International Conference on Machine Learning, ICML 2018, 1, 67–85. Retrieved from https://arxiv.org/abs/1707.02392v1

[2] Ando, H., Niitsu, Y., Hirasawa, M., Teduka, H., & Yajima, M. (2016). Improvements of classification accuracy of film defects by using GPU-accelerated image processing and machine learning frameworks. In Proceedings - NICOGRAPH International 2016, NicoInt 2016 (pp. 83–87). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/NicoInt.2016.15

[3] Assendorp, J. P., & Deep. (2017). Deep learning for anomaly detection in multivariate time series data. The Ninth International Conferences on Pervasive Patterns and Applications Defect 2017, 13(1), 1–12. Retrieved from https://www.metaljournal.com.ua/assets/Journal/english-edition/MMI_2015_7/045

[4] Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184

[5] Christiansen, P., Nielsen, L. N., Steen, K. A., Jørgensen, R. N., & Karstoft, H. (2016). DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors (Switzerland), 16(11). https://doi.org/10.3390/s16111904

[6] Ge, R., Zhu, Y., Xiao, Y., & Chen, Z. (2017). The subway pantograph detection using modified faster R-CNN. In Communications in Computer and Information Science (Vol. 685, pp. 197–204). Springer Verlag. https://doi.org/10.1007/978-981-10-4211-9_20

[7] Han, Z., Wei, B., Mercado, A., Leung, S., & Li, S. (2018). Spine-GAN: Semantic segmentation of multiple spinal structures. Medical Image Analysis, 50, 23–35. https://doi.org/10.1016/j.media.2018.08.005

[8] Jabez, J., & Muthukumar, B. (2015). Detection Approach. Procedia - Procedia Computer Science, 48(Iccc), 338–346. Retrieved from http://dx.doi.org/10.1016/j.procs.2015.04.191

[9] Kaushik, P., & Jain, M. (2018). Design of low power CMOS low pass filter for biomedical application. International Journal of Electrical Engineering & Technology (IJEET), 9(5).

[10] Kaushik, P., Jain, M., & Jain, A. (2018). A pixel-based digital medical images protection using genetic algorithm. International Journal of Electronics and Communication Engineering, 31-37. http://www.irphouse.com/ijece18/ijecev11n1_05.pdf

[11] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application. https://ijsetr.com/uploads/136245IJSETR17012-283.pdf

[12] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[13] Kaushik, P., & Jain, M. (2018). Design of low power CMOS low pass filter for biomedical application. International Journal of Electrical Engineering & Technology (IJEET), 9(5).

[14] Kaushik, P., Jain, M., & Jain, A. (2018). A pixel-based digital medical images protection using genetic algorithm. International Journal of Electronics and Communication Engineering, 31-37. http://www.irphouse.com/ijece18/ijecev11n1_05.pdf

[15] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application. https://ijsetr.com/uploads/136245IJSETR17012-283.pdf

[16] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[17] Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., … Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122-1131.e9. https://doi.org/10.1016/j.cell.2018.02.010

[18] Kim, J. Y., Bu, S. J., & Cho, S. B. (2017). Malware detection using deep transferred generative adversarial networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10634 LNCS, pp. 556–564). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_58

[19] Kim, J. Y., Bu, S. J., & Cho, S. B. (2018). Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Information Sciences, 460–461, 83–102. https://doi.org/10.1016/j.ins.2018.04.092

[20] Kos, J., Fischer, I., & Song, D. (2018). Adversarial examples for generative models. In Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018 (pp. 36–42). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SPW.2018.00014

[21] Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016). Autoencoding beyond pixels using a learned similarity metric. In 33rd International Conference on Machine Learning, ICML 2016 (Vol. 4, pp. 2341–2349). International Machine Learning Society (IMLS).

[22] Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016). Vae/Gan. Icml, 4, 2341–2349.

[23] Li, W., Wu, G., & Du, Q. (2017). Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Letters, 14(5), 597–601. https://doi.org/10.1109/LGRS.2017.2657818

[24] Li, Y., Min, M. R., Shen, D., Carlson, D., & Carin, L. (2018). Video generation from text. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7065–7072). AAAI press. https://doi.org/10.1609/aaai.v32i1.12233

[25] Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. International Conference on Learning Representations, ICLR.

[26] Makhzani, A., & Frey, B. (2017). PixelGAN autoencoders. In Advances in Neural Information Processing Systems (Vol. 2017-December, pp. 1976–1986). Neural information processing systems foundation.

[27] Nagahara, H., Umeda, K., & Yamashita, A. (2017). Thirteenth international conference on quality control by artificial vision 2017. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 10338). https://doi.org/10.1117/12.2277475

[28] Puneet Kaushik, Mohit Jain , Gayatri Patidar, Paradayil Rhea Eapen, Chandra Prabha Sharma (2018). Smart Floor Cleaning Robot Using Android. International Journal of Electronics Engineering. https://www.csjournals.com/IJEE/PDF10-2/64.%20Puneet.pdf

[29] Puneet Kaushik, Mohit Jain , Gayatri Patidar, Paradayil Rhea Eapen, Chandra Prabha Sharma (2018). Smart Floor Cleaning Robot Using Android. International Journal of Electronics Engineering. https://www.csjournals.com/IJEE/PDF10-2/64.%20Puneet.pdf

[30] Sabokrou, M., Fayyaz, M., Fathy, M., & Klette, R. (2017). Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes. IEEE Transactions on Image Processing, 26(4), 1992–2004. https://doi.org/10.1109/TIP.2017.2670780

[31] Sørensen, R. A., Rasmussen, J., Nielsen, J., & Jørgensen, R. N. (2017). Thistle detection using convolutional neural networks: EFITA 2017 Presentation. EFITA WCCA Congress. 2.-6. June 2017, 16(July), 1–15. Retrieved from https://www.mdpi.com/165332

[32] Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., & Sutton, C. (2017). VEEGAN: Reducing mode collapse in GANs using implicit variational learning. In Advances in Neural Information Processing Systems (Vol. 2017-December, pp. 3309–3319). Neural information processing systems foundation.

[33] Stentoumis, C., Protopapadakis, E., Doulamis, A., & Doulamis, N. (2016). A holistic approach for inspection of civil infrastructures based on computer vision techniques. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 41, pp. 131–138). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprsarchives-XLI-B5-131-2016

[34] Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., & Hospedales, T. M. (2018). Learning to Compare: Relation Network for Few-Shot Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1199–1208). IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00131

[35] T., J. (2018). Comparative Study of GAN and VAE. International Journal of Computer Applications, 182(22), 1–5. https://doi.org/10.5120/ijca2018918039

[36] Tawara, N., Kobayashi, T., Fujieda, M., Katagiri, K., Yazu, T., & Ogawa, T. (2018). Adversarial autoencoder for reducing nonlinear distortion. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings (pp. 1669–1673). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.23919/APSIPA.2018.8659540

[37] V.N., E., & U.R., I. (2016). Modeling approaches for time series forecasting and anomaly detection. Animal Molecular Breeding, 8–13.

[38] Wang, H., Qin, Z., & Wan, T. (2018). Text generation based on generative adversarial nets with latent variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10938 LNAI, pp. 92–103). Springer Verlag. https://doi.org/10.1007/978-3-319-93037-4_8

[39] Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018, August 1). Convolutional neural networks: an overview and application in radiology. Insights into Imaging. Springer Verlag. https://doi.org/10.1007/s13244-018-0639-9

[40] Zhao, Y., Deng, B., Huang, J., Lu, H., & Hua, X. S. (2017). Stylized adversarial autoencoder for image generation. In MM 2017 - Proceedings of the 2017 ACM Multimedia Conference (pp. 244–251). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123266.3123450

[41] Zhao, Z., Li, B., Dong, R., & Zhao, P. (2018). A surface defect detection method based on positive samples. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11013 LNAI, pp. 473–481). Springer Verlag. https://doi.org/10.1007/978-3-319-97310-4_54

[42] Zheng, Y. J., Zhou, X. H., Sheng, W. G., Xue, Y., & Chen, S. Y. (2018). Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks, 102, 78–86. https://doi.org/10.1016/j.neunet.2018.02.015

[43] Zhou, Y., & Tuzel, O. (2018). VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 4490–4499). IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00472

[44] Puneet Kaushik, Mohit Jain, Study and Analysis of Image Encryption Algorithm Based on Arnold Transformation. International Journal of Computer Engineering and Technology, 9(5), 2018, pp. 59-63. http://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5

[45] +./34JKLZ[]lmnpqˆ‰Š‹“ïßßïßßïßïïÓÇ»®Ç»®ŸŸ‘�rcQ"hï^0h^~ 5�6�CJOJQJaJhï^0h˜gCJOJQJaJhï^0h^~ CJOJQJaJhï^0h˜dÉ6�OJPJQJ]�hï^0h˜dÉ6�OJQJ]�hï^0h˜dÉ6�H*OJQJ]�hï^0h˜dÉOJPJQJhï^0h˜dÉH*OJQJhï^0h˜dÉOJQJ\�hï^0hï^0OJQJ\�hï^0hï^0CJ(OJQJ\�aJ(hï^0h˜dÉCJ(OJQJ\�aJ(KLn‰Š‹ÃÄ¢£¤±²õõõõàÒÁÁ°°°œ‹$„ñÿdì¤:]„ñÿa$gdï^0$ &F„ñÿdì¤:]„ñÿa$gd¸9±$„ñÿdì¤:]„ñÿa$gd¸9±$„ñÿdì¤:]„ñÿa$gd˜dÉ [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[46] [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@article{1709001,
      author = {Aditya Kinnori, Aniket Tripathi},
      title = {A Comparative Study of Spatial Hierarchies and Pose-Aware Object Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {6},
      pages = {186-197},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709001.pdf},
      abstract = {To understand the contents of a picture, computers in modern computer vision first focus on finding and spotting objects. If the environment is disorganized, people change positions, a part of their body is covered or other small details happen, CNNs find it tougher. Experts are now working with spatial hierarchy-based detection and pose-aware object detection as fresh techniques. The text examines both approaches, noting their key points, how they perform and the types of evaluation they fit for. They consider both the environment and the relationships around items to determine what they are by noticing how they appear and co-occur. They are most effective when there is enough supporting evidence for recognizing things. Another way to state it, pose-aware detection combines feature points and component detection, so non-rigid objects are detected better as their appearance depends on how they jointly move. Lately, Feature Pyramid Networks (FPN) are checked with graphs and Pose-RCNN alongside keypoint prediction are studied on the COCO 2017, PASCAL3D+, MPII and ADE20K datasets. Quantitative research shows that models with body-part awareness perform better than those with a fixed spatial structure and achieve up to 81.2% on keypoint tasks and higher mAP when dealing with dynamic poses. Even so, spatial models can process information fast and are precisely accurate with unchanging, multiple items. We also consider the speed at which a model makes a prediction (inference latency), how complicated the model is, any additional data it uses (annotations) and the extent to which it can be applied. It was determined that models that look at both location and pose can form a good base for future progress. Keep this document so you can learn and guide others about picking object-detection methods for various tasks.},
      keywords = {Spatial hierarchies, Pose-aware detection, Object detection, Deep learning, Convolutional neural networks, Feature representation, Computer vision, Visual perception, Contextual learning, Keypoint estimation.},
      month = {December},
  }