TY  - JOUR
T1  - Real-time Burglar Recognition Based on Human Skeletal Data Using Openpose and Long Short Term Memory Network
AU - Kumarawadu, Priyantha AU - Mohammed Raly, Shadiya 
JO  - Asian Journal of Information Technology
VL  - 21
IS  - 1
SP  - 1
EP  - 5
PY  - 2022
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2022.1.5
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2022.1.5
KW  - Long-short team memory
KW  -open pose
KW  -real time screaming protocol
KW  -2D skeletal data
KW  -burglar recognition
AB  - The recognition of a burglar caught in CCTV
surveillance in real time remains a challenging problem in
the domain of action recognition. Existing security
systems prioritizes the data acquired from sound sensors,
motion sensors, glass breaker sensors over visual sensors
to understand the context behind a sequence of action.
The proposed system uses the Real-Time Streaming
Protocol (RTSP) address of the surveillance camera to
acquire the live surveillance images and then uses Open
Pose which is a real-time person key point detection
library to extract 2D skeletal data which are then fed into
a Long-Short Term Memory (LSTM) model, Recurrent
Neural Netowrk (RNN) model and Gated Recurrent Unit
(GRU) model for classification. The experimental results
showed that the performance of LSTM based classifier
out performed against RNN and GRU based classifiers
under various burglar actions and it was were promising
with a training and a validation accuracies of 92.3% and
86.5%, respectively.
ER  -