Indhumathi .J

Work place: Annamalai University Department of Computer Science and Engineering, Chidambaram, Annamalai Nagar - 608002, India



Research Interests: Computer Architecture and Organization, Computational Learning Theory, Computer systems and computational processes, Graph and Image Processing, Image Processing


J. Indhumathi is a PhD (full-time) Research Scholar in the Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram(India). She received her Master of Engineering (M.E) degrees in the year of 2019 and Bachelor of Engineering (B.E) degrees in the year of 2017 from the Department of Computer Science and Engineering, Annamalai University(India). Her area of research interest includes image and video processing, Machine and Deep Learning

Author Articles
Real-Time Video based Human Suspicious Activity Recognition with Transfer Learning for Deep Learning

By Indhumathi .J Balasubramanian .M Balasaigayathri .B

DOI:, 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%. 

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