REAL TIME MOVING OBJECT DETECTION USING C-MODEL WITH CNN
Author(s):
Madhulata Silawat, Abhishek Pandey
ISSN:
2349-6002
Cite This Article:
REAL TIME MOVING OBJECT DETECTION USING C-MODEL WITH CNNInternational Journal of Innovative Research in Technology(www.ijirt.org) ,ISSN: 2349-6002 ,Volume 6 ,Issue 4 ,Page(s):164-168 ,September 2019 ,Available :IJIRT148641_PAPER.pdf
Keywords:
Detection of moving objects; tracking of moving objects; behavior understanding, Neural Network, Caffe model, CNN.
Abstract
The object detection and tracking is the important steps of computer vision algorithm. The robust object detection is the challenge due to variations in the scenes. Another biggest challenge is to track the object in the occlusion conditions. Neural Networks has become one of the most demanded areas of Information Technology and it has been successfully applied to solving many issues of Artificial Intelligence, for example, speech recognition, computer vision, natural language processing, and data visualization. This thesis describes the developing the neural network model for object detection and tracking. The understanding of moving object based on vision has also developed rapidly. Its related technologies have been widely used in public transportation, square, government, bank and other scenes. At present, there are commonly used algorithms in moving object detection, including the difference method (background difference method and time difference method) and optical flow method and neural network. The difference method was based on the current video and the reference image subtraction to complete the detection. Hence in this approach, the moving objects detection using Caffe framework has been proposed. A novel Fast CNN based object tracking algorithm is used for robust object detection. The proposed approach is able to detect the object in different illumination and occlusion.
Article Details
Unique Paper ID: 148641

Publication Volume & Issue: Volume 6, Issue 4

Page(s): 164 - 168
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Last Date 25 December 2019


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