Deep Learning and Approach for Tracking People’s Movements in a Video (original) (raw)

Real Time Human Activity Recognition Using Deep Learning

International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024

With the increasing number of anti-social products, security is now given more importance. Many organizations have installed CCTV to monitor people and their interactions at all times. For a developed country of 64 million people, each person is caught on camera 30 times a day. A large amount of video data was generated and stored for a specific period of time. A 704x576 image recorded at 25fps will generate about 20GB per day. Constantly monitoring data to judge whether events are abnormal is a nearly impossible task because it requires constant management and attention. This makes it necessary to automate the same. Additionally, it is important to identify which frames and which fractions contain abnormal activity which helps to quickly decide if the abnormal activity is abnormal This is done by rotating video frames and the individuals and their activity types analyzed from the processed frame. Machine learning and deep learning algorithms and methods help us in widespread adoption to make this possible.

A Survey Paper on Moving Object Detection Using Deep Learning

International Journal of Advanced Research in Science, Communication and Technology

Moving object detection in Python using deep learning is a powerful technique for accurately identifying and localizing moving objects in images or videos. By leveraging pre-trained models like YOLO or SSD, developers can implement this task efficiently. The Python implementation allows for customization and extension to handle real-time video streams and complex scenarios. This approach is valuable for researchers, practitioners, and enthusiasts interested in moving object detection using deep learning. Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. This survey paper provides study of various methodologies for object detection. This paper provides systematic analysis of various existing object detection techniques with precise and arranged representation.

A review on Human Action Recognition in videos using Deep Learning

2021

Human Action Recognition (HAR) in video plays a vital role in today's world. The aim of HARis to automatically identify and analyse human activities using acquired information from video data. Some of the applications include security and surveillance, smart homes and assisted living, health monitoring, robotics, human– computer interaction, intelligent driving, video-retrieval, gaming and entertainment etc. This paper explores the impact of Deep Learning techniques on action recognition. We also explore how spatiotemporal features are aggregated through various deep architectures, the role of optical flow as an input, the impacts on real-time capabilities, and the compactness & interpretability of the learned features. Although several papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review ...