Detecting unattended packages through human activity recognition and object association

This paper provides a novel approach to detect unattended packages in public venues. Different from previous works on this topic which are mostly limited to detecting static objects where no human is nearby, we provide a solution which can detect an unattended package with people in its close proximity but not its owners. Mimicking the human logic in detecting such an event, our decision-making is based on understanding human activity and the relationships between humans and packages. There are three main contributions from this paper. First, an efficient method is provided to online categorize moving objects into the predefined classes using the eigen-features and the support vector machines (SVM). Second, utilizing the classification results, a method is developed to recognize human activities with hidden Markov models (HMM) and decide the package ownership. Finally the unattended package detection is achieved by analyzing multiple object relationships: package ownership, spatial and temporal distance relationships.