Body part boosting model for carried baggage detection and classification

Abstract

In the automatic video surveillance system, the detection of a human carrying baggage is a potentially important objective for security and monitoring purposes in the public spaces. This paper introduces a new approach for detecting and classifying baggage carried by a human on the images. It utilizes the spatial information of the baggage in reference to the body of the human carrying it. A human-baggage detector is modeled by the body parts of a human, including the head, torso, leg, and baggage parts. The feature descriptors are extracted for each part based on its characteristics and these features are further trained using a support vector machine (SVM) classifier. A mixture model is built specifically for the baggage part due to a significant variation in shape, size, color, and texture. The boosting strategy constructs a strong classifier by combining a set of weak classifiers which are obtained by training the body part. The proposed method has been extensively evaluated using the public datasets. The experimental results confirm that the proposed method is viable for a state-of-the-art in the carried baggage detection and classification system.