Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle

Abstract

In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentally validated, and the results show that the process is sufficiently precise, faster, and outperforms the existing methods. We achieved an accuracy of between 96% and 98.75% for the binary classification and 96.67% and 98.33% for the multi-class classification. For multi-class classification, the datasets, such as PETA, INRIA, ILIDS, and MSMT17, are re-annotated with one’s direction information about one’s stance to test the suggested approach’s efficacy.