• Alefiya Laturwala
• Komal Jadhav
• Riya Kumar
• Shikha Bhaskar

Computer vision is becoming increasingly important in video surveillance, video retrieval and analysis, and human-computer interaction because of the ubiquitous nature of video data. Due to rising demand for automated analysis of human actions by computers, this project is being undertaken. This paper presents a human pose estimation system based on global feature extractions that can operate in real time. In the proposed method, video sequences are used to compute features of images. A real-time human pose estimation system is the goal. UT Actions such as hugging, handshakes, kicking, pushing and punching are included in the dataset. Real-time detection of various human activities is made possible through the use of an SVM classifier. The dataset which we have been used is to find similar videos based on the actions in the input videos.

Support Vector Machine (SVM)

"Human Pose Estimation Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.g310-g317, April-2022, Available :http://www.jetir.org/papers/JETIR2204647.pdf

"Human Pose Estimation Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppg310-g317, April-2022, Available at : http://www.jetir.org/papers/JETIR2204647.pdf