Robust Baggage Detection and Classification Based on Local Tri-directional Pattern
This work addresses security monitoring in public places by improving baggage detection, but it is incremental as it builds upon existing Local Binary Pattern methods.
The paper tackled the problem of inaccurate and large feature descriptions in baggage detection by proposing a Local tri-directional pattern descriptor for extracting features from human body parts, achieving superior performance over state-of-the-art methods on INRIA and MSMT17 V1 datasets.
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.