Subjective Logic Based Hybrid Approach to Conditional Evidence Fusion for Forensic Visual Surveillance

In forensic analysis of visual surveillance data, conditional knowledge representation and inference under uncertainty play an important role for deriving new contextual cues by fusing relevant evidential patterns. To address this aspect, both rule-based (aka. extensional) and state based (aka. intensional) approaches have been adopted for situation or visual event analysis. The former provides flexible expressive power and computational efficiency but typically allows only one directional inference. The latter is computationally expensive but allows bidirectional interpretation of conditionals by treating antecedent and consequent of conditionals as mutually relevant states. In visual surveillance, considering the varying semantics and potentially ambiguous causality in conditionals, it would be useful to combine the expressive power of rule-based system with the ability of bidirectional interpretation. In this paper, we propose a hybrid approach that, while relying mainly on a rule-based architecture, also provides an intensional way of on-demand conditional modeling using conditional operators in subjective logic. We first show how conditionals can be assessed via explicit representation of ignorance in subjective logic. We then describe the proposed hybrid conditional handling framework. Finally we present an experimental case study from a typical airport scene taken from visual surveillance data.