Online ISSN: 2515-8260

Keywords : Fall detection

Identification and Detection of Abnormal Human Activities using Deep Learning Techniques


European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 408-417

In recent years, it is in public to use the surveillance cameras for continuous monitoring of public and private spaces because of increasing crime. Most current surveillance systems need a human operator to constantly watch them and are ineffective as the amount of video data is increasing day by day. Surveillance cameras will be more useful tools if instead of passively recording; they generate warnings or real-time actions when unusual activity is detected. But recognizing and classifying human activity as normal or abnormal from a live video stream is a stimulating job in the pitch of CPU vision. There is a need for a smart surveillance system for the automatic identification of abnormal behaviour of humans for a specific-scene. Presentpaperstretches an overview of different machine learning methods used in recent years to develop such a model. It also gives an exposure to the recent works in the field of anomaly detection in surveillance video and its applications

Video Based Fall Detection Using Deep Convolutional Neural Network

Gangireddy Prabhakar Reddy; M. Kalaiselvi Geetha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5542-5551

Falling often causes deadly conditions such as unconsciousness and related injuries among the elderly population if failing provided with aid and caretakers nearby. In this context, an automatic fall monitoring system gains its popularity by solving the problem with immediate prompting, thereby allowing the caretakers and other persons to get activated with an alarm message. It assists older adults in living without fear of falling and being independent in society. In recent decades, vision-based fall monitoring receiving attention among research communities for its diversified features. It helps identify the human in the intended regions, and by using the collected phenomenon from the area, it trains the fall recognition classifiers. Besides, human detection errors and lack of massive-scale datasets make the vision-based fall monitoring face challenges like robustness and efficiency in performing generalization to invisible regions. Hence a robust learning and classification system is reasonably needed to combat the challenges. In this proposed system, automatic fall detection using deep learning is modeled using RGB images gathered from the single-camera source. More significantly, it determines the sensitive details that prevailed in the original images and ensures privacy, widely considered for safety and protection. Various experiments are carried out using real-time world fall data sets. The results show that the system enhances fall recognition awareness and achieves a high F-Score by performing high accurate fall detection from real-world environments.