  In recent decades, the automatic video surveillance system has gained
significant importance in computer vision community. The crucial objective of
surveillance is monitoring and security in public places. In the traditional
Local Binary Pattern, the feature description is somehow inaccurate, and the
feature size is large enough. Therefore, to overcome these shortcomings, our
research proposed a detection algorithm for a human with or without carrying
baggage. The Local tri-directional pattern descriptor is exhibited to extract
features of different human body parts including head, trunk, and limbs. Then
with the help of support vector machine, extracted features are trained and
evaluated. Experimental results on INRIA and MSMT17 V1 datasets show that
LtriDP outperforms several state-of-the-art feature descriptors and validate
its effectiveness.
