Abnormal Activity Recognition Based on Deep Learning
Abnormal Activity Detection is a crucial monitoring which is used to record anomalous human behavior without human efforts, the model has the ability to automatically take input video and live surveillance suspicious activity. The main objective is to automatically identify and mark occurrences that need human attention using Machine Learning and deep learning algorithms. The concept be applied to video surveillance system to quickly identify unusual activities. The model detecting on typical patterns of behavior in a given environment.
An Automated Activity Recognition System is required to identify the basic day to day activities performed by human being. The activities of human being can be generally categorized into anomalous activity or normal activity. Activity models required for classification and identification of human activities.
In this project a CNN approach that classifies and detects suspicious activities in the input videos. Model is trained on large dataset of images and videos and then test on any input video with testing script. Machine Learning Model is integrated with User Interface that is developed using Tkinter.
Abnormal activity detection in videos is a fascinating area of computer vision that aims to automatically identify unusual or anomalous events from video footage. By leveraging machine learning algorithms and computer vision techniques, it becomes possible to develop systems that can detect abnormal activities, such as fighting, robbery, abuse, or suspicious behavior, in real-time.
Violence Detection Using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
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