Applications of Factorization Theorem and Ontologies for Activity ModelingRecognition and Anomaly Detection

In this thesis two approaches for activity modeling and suspicious activity detection are examined. First is application of factorization theorem extension for deformable models in two dierent contexts. First is human activity detection from joint position information, and second is suspicious activity detection for tarmac security. It is shown that the first basis vector from factorization theorem is good enough to dierentiate activities for human data and to distinguish suspicious activities for tarmac security data. Second approach dierentiates individual components of those activities using semantic methodol- ogy. Although currently mainly used for improving search and information retrieval, we show that ontologies are applicable to video surveillance. We evaluate the domain ontologies from Challenge Project on Video Event Taxonomy sponsored by ARDA from the perspective of general ontology design principles. We also focused on the eect of the domain on the granularity of the ontology for suspicious activity detection

Active shooters — how close are they?

Recent terrorist attacks in the United States, Canada and Western Europe have shown an increase in the incidence of “Active Shooters” [1]. These ruthless and desperate assassins usually attack urban and poorly protected areas (lack of armed protection) that are densely populated [2]. Utilizing their strength, they realize that their plan is to maximize the number of casualties, without counting on the consequences of their actions. The basis of their action may be based on extremely radical views. Frequent outcomes for active shooters include suicide during an attack (90%) or the resolution of the threat by the authorities [3]. In response to the ever-increasing number of assassinations and the risk of such incidents in one’s immediate surroundings, comprehensive education should be widely spread. Thus, it is important to promote appropriate behaviour, rules of reaction during an attack by an armed assailant, as well as cooperation with incoming service personnel. Such actions will not only help one prevent, but also allow one to prepare for such incidences

Can CCTV reliably detect gun crime?

In recent years the highest levels of gun crime in England and Wales have been recorded. Gun crime is known to occur in environments that fall under the view of the networks of CCTV cameras which are endemic in urban Britain. Consequently, CCTV has the potential to detect gun crime. However, CCTV operators face a near impossible task: often, each individual operator is required to monitor multiple cameras for extended time periods. It is not surprising then to find that CCTV has had little effect at reducing gun crime. A new collaborative research programme (MEDUSA) is underway across the UK which will develop a software application to aid CCTV operators detect the precursors of gun crime. MEDUSA aims automatically to detect both guns as objects (concealed or otherwise) and people who intend to commit gun crime, via CCTV. The system is partly based on eliciting the features that skilled CCTV operators identify as indicative of a concealed weapon or suspicious behaviour related to gun crime. Initial studies into the examination of these skills across several CCTV centres indicate that there was little formal instruction in the detection of mal-intent. Much skill is built up from prolonged experience in examining CCTV imagery. Thus, potentially, there is widespread individualisation of detection skills. Further, visual cues specific to gun crime were limited. However, the present methodology relied upon direct report. It is argued that a range of both consciously and subconsciously detected cues could facilitate gun detection via CCTV. Future studies will examine this possibility