Abstract:
Intrusion Detection System(IDS) is a monitoring system that identify un usual activity and send out alerts when it does. It is a core part of site’s
safety and security strategy. A computer vision task that requires recogniz ing, associating, and tracking semantic key points is human posture estima tion and tracking. Human Action Recognition (HAR) methods are gaining
importance, with the emerging advancements in computer vision and pat tern recognition. There are various methods present for action recognition
and each technique has its advantages and disadvantages. Despite being a lot
of research work, action recognition is still a challenging and complex task.
The precision of posture estimate has been limited because of the significant
processing resources required for semantic keypoint tracking in live video
data. Human Pose Estimation is a core problem for understanding people in
videos and images. This project presents an effective method for improving
existing security systems and to further automate the threat prevention and
protection procedure. This method identifies human wall climbing activity,
that occur within the video range and detects unlawful or suspicious activi ties, alert if detected. This work is divided into two parts; multi person pose
estimation and action recogonition and alerting using estimated pose. Open Pose library is used for the realtime 2D pose estima- tion and it consists of
recognition of 18 body key points and joint locations and a final parsing to
get skeleton. These are further used to extract robust motion features, then
a Convolution Neural Network (CNN) is used to recognize the activities as sociated with these features. Different subjects from different camera angles
are used to make the approach person-independent. The proposed method
shows best result with promising performance, reaching an overall accuracy
of 91.6%
