Work place: Annamalai University/ Department of Computer Science and Engineering, Chidambaram, Annamalai Nagar - 608002, India
Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Architecture and Organization, Image Processing, Data Structures and Algorithms
Balasaigayathri .B is a Master of Engineering (M.E) in the Department of Computer Science and Engineering, Annamalai University(India). She is currently working as an Assistant Professor in the Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology (Deemed to be University) (India). She received her Bachelor of Engineering (B.E) degrees in the Department of Computer Science and Engineering, Annamalai University. Her area of work is image and video processing and Deep Learning.
DOI: https://doi.org/10.5815/ijigsp.2023.01.05, Pub. Date: 8 Feb. 2023
Nowadays, the primary concern of any society is providing safety to an individual. It is very hard to recognize the human behaviour and identify whether it is suspicious or normal. Deep learning approaches paved the way for the development of various machine learning and artificial intelligence. The proposed system detects real-time human activity using a convolutional neural network. The objective of the study is to develop a real-time application for Activity recognition using with and without transfer learning methods. The proposed system considers criminal, suspicious and normal categories of activities. Differentiate suspicious behaviour videos are collected from different peoples(men/women). This proposed system is used to detect suspicious activities of a person. The novel 2D-CNN, pre-trained VGG-16 and ResNet50 is trained on video frames of human activities such as normal and suspicious behaviour. Similarly, the transfer learning in VGG16 and ResNet50 is trained using human suspicious activity datasets. The results show that the novel 2D-CNN, VGG16, and ResNet50 without transfer learning achieve accuracy of 98.96%, 97.84%, and 99.03%, respectively. In Kaggle/real-time video, the proposed system employing 2D-CNN outperforms the pre-trained model VGG16. The trained model is used to classify the activity in the real-time captured video. The performance obtained on ResNet50 with transfer learning accuracy of 99.18% is higher than VGG16 transfer learning accuracy of 98.36%.[...] Read more.
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