Document Type
Journal Article
Publication Title
International Journal of Advanced Computer Science and Applications
Volume
12
Issue
1
First Page
154
Last Page
157
Publisher
The Science and Information (SAI) Organization
School
School of Engineering
RAS ID
38862
Abstract
© 2021. All rights reserved. Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers. The first layer, which is the input image, is of 100 * 100 size. The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling. The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms.
DOI
10.14569/IJACSA.2021.0120118
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Mohammed, H. R., & Hussain, Z. M. (2021). Detection and recognition of moving video objects: Kalman filtering with deep learning. International Journal of Advanced Computer Science and Applications, 12(1), 154-157. https://doi.org/10.14569/IJACSA.2021.0120118