Automatic baggage detection and classification

In this paper we propose an enhanced approach to automatic baggage detection in CCTV video footage that addresses some of the shortcomings of state-of-the-art algorithms and a novel approach to the subsequent recognition of their type. The proposed approach consists of standard steps used in baggage detection, namely, the estimation of moving direction of humans carrying baggage and the alignment of a temporal human-like template with the best matched view-specific exemplars. In addition, unique to the proposed approach is the connected baggage separation algorithm to achieve accurate baggage detection. A further key novel contribution is baggage type classification which is based on the determination of the position of the bag in relevance to the human body carrying it. The proposed system has been extensively tested for its effectiveness on PETS 2006 dataset and additional CCTV video footage captured to cover specific test scenarios. The experimental results suggest that the proposed algorithm is capable of superseding the functional performance of state-of-the art baggage detection algorithms and is able to achieve accurate baggage type classification.