TY  - JOUR
T1  - Burglar Detection using Deep Learning Techniques
AU - Jin Kwon, Se AU - Riaz, Rabia AU - Shahla Rizvi, Sanam AU - Mushtaq, Ayesha AU - Shokat, Sana 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 8
SP  - 2672
EP  - 2686
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.2672.2686
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.2672.2686
KW  - Burglar detection
KW  -security
KW  -intrusion detection
KW  -convolution neural networks
KW  -deep neural
networks
KW  -human intrusion detection
KW  -object detection
AB  - Burglar detection security systems have become a necessity in this age because of the increasing
break-in cases in urban cities thus making these systems essential for residential as well as office usage. This
study investigated how to model an intrusion detection system based on deep learning. Two deep learning
approaches named generic Deep Neural Networks (DNN) and Convolution Neural Networks (CNN) are used
for the training of the dataset. The experimental results showed that CNN approach is more suitable for burglar
detection as it gives high accuracy with a superior performance as compared to the generic DNN approach.
CNN provides a new research method with the improved accuracy of human intrusion detection. Experimental
results found that CNN is compatible to solve classification problems and significantly faster and precise as
compared to traditional object detection methods.
ER  -