A reasoning approach to enable abductive semantic explanation upon collected observations for forensic visual surveillance

This paper proposes an approach to enable automatic generation of probable semantic hypotheses for a given set of collected observations for forensic visual surveillance. As video analytic power exploited in visual surveillance is getting matured, the more automatically generated intermediate semantic metadata became available. In the sense of forensic reuse of such data, the majority of approaches have been focused on specific semantic query based scene analysis. However, in reality, there are often cases in which it is more natural to reason about the most probable semantic explanation of a scene given a collection of specific semantic evidences. In general, this type of diagnostic reasoning is known as abduction. To enable such a semantic reasoning, in this paper, we propose a layered reasoning pipeline that combines abductive logic programming together with backward and forward chaining based deductive logic programming. To rate derived hypotheses, we apply subjective logic. We present a conceptual case study in a distributed camera based scenario. The case study shows the potential and feasibility of the proposed approach for forensic analysis of visual surveillance data.