Asian Journal of Computer Science and Technology (AJCST)
RetinaNet Based Environment ClassificationAuthor : R. Balamurugan, R. Arunkumar and S. Mohan
Volume 7 No.1 Special Issue:November 2018 pp 112-114
Environmental classification is very useful for visually impaired persons and Robotic applications. The main objective of this work is to detect and recognize the objects present in a scene and identify the environment based on the occurrence probability of the objects in the scene. Objects from the real-time images are detected and recognized by means of RetinaNet. Occurrence probabilities of the recognized objects are used to identify the environment.
Object Detection, Object Recognition, Retina Net
 T.Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection” In CVPR, 2017.
 Janardhana Rao, and O. Venkata Krishna, “The Log Polar Transformation for Rotation Invariant Image Registration of Aerial Images”, IJCTA, Vol. 4, pp. 833-840, 2013.
 R. Arunkumar, M. Balasubramanian, and S. Palanivel, “Indoor Object Recognition System using Combined DCT-DWT under Supervised Classifier”, IJCA., Vol. 82 – No3, pages: 17- 21, November 2013
 J. Dai, Y. Li, K. He, and J. Sun., “R-FCN: Object detection via region-based fully convolutional networks”, In NIPS., 2016.
 A. Wahi, P. Ravi, M. Saranya, “A neural network approach to rotated object recognition based on edge features: Recognition rate and CPU time improvement for rotated object recognition using DWT”, Computing Communication and Networking Technologies (ICCCNT), Vol. 29-31, pp. 1 – 6, July 2010.
 Junliang Li, Hon-Cheng Wong, Member, IEEE, Sio-Long Lo, and Yuchen Xin, “Multiple Object Detection by a Deformable Part-Based Model and an R-CNN”.
 O. Barinova, V. Lempitsky, and P. Kholi, “On detection of multiple object instances using hough transforms”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 34, No. 9, pp. 1773–1784, Sep. 2012
 R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation”, In CVPR., 2014
 S. R. Bulo, G. Neuhold, and P. Kontschieder, “Loss maxpooling for semantic image segmentation”, In CVPR, 2017
 S. Bell, C. L. Zitnick, K. Bala, and R. Girshick, “Insideoutside net: Detecting objects in context with skip pooling and recurrent neural networks”, In CVPR., 2016
 Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollar, “Focal Loss for Dense Object Detection”, Feb 2018
 Jian Wu, Zhiming Cui, Victor S. Sheng, Pengpeng Zhao, Dongliang Su, and Shengrong Gong, “A Comparative Study of SIFT and its Variants , Measurment Science Review., Vol 13, No. 3, 2013
 Nasser H. Dardas, and Nicolas D. Georganas, “Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques”, IEEE Trans. on Instrumentation and Measurement., Vol. 60, No. 11, November 2011
 Seyyid Ahmed Medjahed, “A Comparative Study of Feature Extraction Methods in Images Classification”, IJIGSP, Vol. 7, No. 3, pp. 16-23, 2015.
 Olga Barinova, Victor Lempitsky , Pushmeet Kholi, “On detection of multiple object instances using hough transforms”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 34, No. 9, pp. 1773–1784, Sep. 2012
 Yang Liu, Lei Huang, Xianglong Liu, and Bo Lang, “A Novel rotation adaptive object detection method based on pair Hough model”, Neurocomputing., Vol. 194, pp. 246-259, 2016.
 Chunsheng Liu, Faliang Chang, and Chengyun Liu, “Cascaded split-level color Haar-like features for object detection”, Electronics Letters., Vol. 51, No. 25, pp. 2106–2107, 10th December 2015.
 Sanjivani Shantaiya, Keshri Verma, and Kamal Mehta, “A Survey on Approaches of Object Detection”, International Journal of Computer Applications, Vol. 65, No.18, pp. 0975 – 8887, March 2013.