Human Activity Classification for Surveillance using Machine Learning and Image Processing Algorithm
DOI:
https://doi.org/10.26438/ijcse/v7i6.343346Keywords:
Human activity recognition, relevance vector classifier, histogram of gradients, Background subtractionAbstract
Today`s world surveillance system plays a major role in the security industry. In the video monitoring system, moving object detection was frequently used. Motion estimation is also an important part of video processing monitoring, such as video filtering and compression of video frames. Video Surveillance System is a powerful tool for tracking people and their public safety operations. The reason for having a monitoring system is not only to place cameras in the human eye place, but also to allow them to automatically acknowledge activities. This paper creates a smart recognition of the system of human activity. At each stage of the suggested system, image processing techniques are used A system was built based on the Caltech database of human activity features acquired from frame sequences. Relevance Vector classifier used in the dataset to classify the model of activity. Classification results show high effectiveness throughout the training, testing and validation stages.
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