Effective Video Saliency Mapping for Object Detection using Deep Learning Neural Networks
R. Vedha Priyavadhana1, G. Shanmuga Priya2, N. Renee Reddiar3, M. Mano Priya4

1Dr. R. Vedhapriyavadhana, Associate Professor/ECE, Francis Xavier Engineering College, Tirunelveli, India.
2G. Shanmuga Priya, PG Scholar/ECE, Francis Xavier Engineering College, Tirunelveli, India.
3N. Renee Reddiar, PG Scholar/ECE, Francis Xavier Engineering College, Tirunelveli, India.
4M. Mano Priya, PG Scholar/ECE, Francis Xavier Engineering College, Tirunelveli, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 471-477 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7493038620 /2020©BEIESP | DOI: 10.35940/ijrte.F7493.038620

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Abstract: In this paper, we propose a new novel DNN-based video saliency prediction method. The main aim of this project is to separating foreground and background images and also finding the motion of the objects. The methodogy used in this project is to seperating the foreground images using background modelling techniques(Pixel searching algorithm).The another technique is Homography based search algorithm for block based motion estimation is used to finding the movement of the objects. Saliency object detection is also used to highlighting the particular region in the images. In our base paper[1],context subtraction algorithm is introduced. But in this paper, Convolutional neural networks is used to classifying the foreground objects from background images. We propose an deep convolutional neural network (CNN) Classifier is used to predict intra-frame salience by exploring object and object movement details .The CNN classifier is also used to separating the new objects from background. We also find from our database that a temporary correlation of human attention exists, with a smooth transition of saliency across video frames. Therefore, in our DNN-based approach we create a two-layer convolutionary long-term memory (2C-LSTM) network, using the extracted CNN features as the data. The fuzzy clustering is used to store the information as the processed images in this project. This project can be implemented in real time applications such as defence, forest and miltrary area. Saliency Mapping is also used to reduce the Zipper effect,it mainly occurs in edges. It gives High resolution too. Finally, the proposed system is developed using matlab simulation. The PSNR value is attained above 95%.
Keywords: Convolution Neural Network (CNN), Convolution Long Short-Term Memory (2C-LSTM) Network, Region Based Pixel Separation(Rot),Fuzzy Cluster.
Scope of the Article: Deep learning.