Graeme A. Jones – författare

Monitoring of public and private sites has increasingly become a very sensitive issue resulting in a patchwork of privacy laws varying from country to country -though all aimed at protecting the privacy of the citizen. It is important to remember, however, that monitoring and vi­ sual surveillance capabilities can also be employed to aid the citizen. The focus of current development is primarily aimed at public and cor­ porate safety applications including the monitoring of railway stations, airports, and inaccessible or dangerous environments. Future research effort, however, has already targeted citizen-oriented applications such as monitoring assistants for the aged and infirm, route-planning and congestion-avoidance tools, and a range of environment al monitoring applications. The latest generation of surveillance systems has eagerly adopted re­ cent technological developments to produce a fully digital pipeline of digital image acquisition, digital data transmission and digital record­ ing. The resultant surveillance products are highly-fiexihle, capahle of generating forensic-quality imagery, and ahle to exploit existing Internet and wide area network services to provide remote monitoring capability.

Monitoring of public and private sites has increasingly become a very sensitive issue resulting in a patchwork of privacy laws varying from country to country -though all aimed at protecting the privacy of the citizen. It is important to remember, however, that monitoring and vi­ sual surveillance capabilities can also be employed to aid the citizen. The focus of current development is primarily aimed at public and cor­ porate safety applications including the monitoring of railway stations, airports, and inaccessible or dangerous environments. Future research effort, however, has already targeted citizen-oriented applications such as monitoring assistants for the aged and infirm, route-planning and congestion-avoidance tools, and a range of environment al monitoring applications. The latest generation of surveillance systems has eagerly adopted re­ cent technological developments to produce a fully digital pipeline of digital image acquisition, digital data transmission and digital record­ ing. The resultant surveillance products are highly-fiexihle, capahle of generating forensic-quality imagery, and ahle to exploit existing Internet and wide area network services to provide remote monitoring capability.

Monitoring of public and private sites has increasingly become a very sensitive issue resulting in a patchwork of privacy laws varying from country to country -though all aimed at protecting the privacy of the citizen. It is important to remember, however, that monitoring and vi­ sual surveillance capabilities can also be employed to aid the citizen. The focus of current development is primarily aimed at public and cor­ porate safety applications including the monitoring of railway stations, airports, and inaccessible or dangerous environments. Future research effort, however, has already targeted citizen-oriented applications such as monitoring assistants for the aged and infirm, route-planning and congestion-avoidance tools, and a range of environment al monitoring applications. The latest generation of surveillance systems has eagerly adopted re­ cent technological developments to produce a fully digital pipeline of digital image acquisition, digital data transmission and digital record­ ing. The resultant surveillance products are highly-fiexihle, capahle of generating forensic-quality imagery, and ahle to exploit existing Internet and wide area network services to provide remote monitoring capability.

With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in people’s daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without difficulty, interpret and r- ognize thousands of events in videos, despite high level of video object clutters, different types of scene context, variability of motion scales, appearance changes, occlusions and object interactions. For a computer vision system, it has been be very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under - tion clutters, event interpretation under complex scenes, multi-level semantic event inference, putting events in context and multiple cameras, event inference from object interactions, etc. In recent years, steady progress has been made towards better models for video event categorisation and recognition, e. g. , from modelling events with bag of spatial temporal features to discovering event context, from detecting events using a single camera to inferring events through a distributed camera network, and from low-level event feature extraction and description to high-level semantic event classification and recognition. Nowadays, text based video retrieval is widely used by commercial search engines. However, it is still very difficult to retrieve or categorise a specific video segment based on their content in a real multimedia system or in surveillance applications.

With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in people’s daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without difficulty, interpret and r- ognize thousands of events in videos, despite high level of video object clutters, different types of scene context, variability of motion scales, appearance changes, occlusions and object interactions. For a computer vision system, it has been be very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under - tion clutters, event interpretation under complex scenes, multi-level semantic event inference, putting events in context and multiple cameras, event inference from object interactions, etc. In recent years, steady progress has been made towards better models for video event categorisation and recognition, e. g. , from modelling events with bag of spatial temporal features to discovering event context, from detecting events using a single camera to inferring events through a distributed camera network, and from low-level event feature extraction and description to high-level semantic event classification and recognition. Nowadays, text based video retrieval is widely used by commercial search engines. However, it is still very difficult to retrieve or categorise a specific video segment based on their content in a real multimedia system or in surveillance applications.