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Facial Emotion Recognition Data Augmentation using Generative Adversarial Network
http://doi.org/10.5626/JOK.2021.48.4.398
The facial emotion recognition field of computer vision has recently been identified to demonstrate meaningful results through various neural networks. However, the major datasets of facial emotion recognition have the problem of “class imbalance,” which is a factor that degrades the accuracy of deep learning models. Therefore, numerous studies have been actively conducted to solve the problem of class imbalance. In this paper, we propose “RDGAN,” a facial emotion recognition data augmentation model that uses a GAN to solve the class imbalance of the FER2013 and RAF_single that are used as facial emotion recognition datasets. RDGAN is a network that generates images suitable for classes by adding expression discriminators based on the image-to-image translation model between the existing images as compared to the prevailing studies. The dataset augmented with RDGAN showed an average performance improvement of 4.805%p and 0.857%p in FER2013 and RAF_single, respectively, compared to the dataset without data augmentation.
Anomaly Detection by a Surveillance System through the Combination of C3D and Object-centric Motion Information
Seulgi Park, Myungduk Hong, Geunsik Jo
http://doi.org/10.5626/JOK.2021.48.1.91
In the existing closed-circuit television (CCTV) videos, the deep learning-based anomaly detection reported in the literature detected anomalies using only the object"s action value. For this reason, it is difficult to extract the action value of an object depending upon the situation, and there is a problem that information is reduced over time. Since the cause of abnormalities in CCTV videos involves several factors such as frame complexity and information according to time series analysis, there is a limit to detecting an abnormality using only the action value of the object. To solve this problem, in this paper, we designed a new deep learning-based anomaly detection model that combined optical flow with C3D to use various feature values centered on the objects. The proposed anomaly detection model used the UCF-Crime dataset, and the experimental results achieved an area under the curve (AUC) of 76.44. Compared to previous studies, this study worked more effectively in fast-moving videos such as explosions. Finally, we concluded that it was appropriate to use the information according to different feature values and time series analysis considering various aspects of the behavior of an object when designing an anomaly detection model.
Backbone Network for Object Detection with Multiple Dilated Convolutions and Feature Summation
Vani Natalia Kuntjono, Seunghyun Ko, Yang Fang, Geunsik Jo
http://doi.org/10.5626/JOK.2018.45.8.786
The advancement of CNN leads to the trend of using very deep convolutional neural network which contains more than 100 layers not only for object detection, but also for image segmentation and object classification. However, deep CNN requires lots of resources, and so is not suitable for people who have limited resources or real time requirements. In this paper, we propose a new backbone network for object detection with multiple dilated convolutions and feature summation. Feature summation enables easier flow of gradients and minimizes loss of spatial information that is caused by convolving. By using multiple dilated convolution, we can widen the receptive field of individual neurons without adding more parameters. Furthermore, by using a shallow neural network as a backbone network, our network can be trained and used in an environment with limited resources and without pre-training it in ImageNet dataset. Experiments demonstrate we achieved 71% and 38.2% of accuracy on Pascal VOC and MS COCO dataset, respectively.
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