NED University Journal of Research
ISSN 2304-716X


E-ISSN 2706-5758


CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

Author(s): Shahbano1, Syed Adnan Shah2, Wakeel Ahmad3, Muhammad Ilyas4
1 Researcher, Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan, Ph. +923310444149, Email: shahbano.ather@gmail.com.
2 Assistant Professor, Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan, Ph. +923425092978, Email: syed.adnan@uettaxila.edu.pk.
3 Lecturer, Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan, Ph. +923335589037, Email: wakeel.ahmad@uettaxila.edu.pk.
4 Assistant Professor, Department of Computer Science and IT, University of Sargodha, Pakistan, Ph. +923009639388, Email: muhammad.ilyas@uos.edu.pk.

https://doi.org/10.35453/NEDJR-ASCN-2019-0100


Volume: XVII

No. 4

Pages: 81-95

Date: September 2020

Abstract:
Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.

Full Paper | Close Window      X |