Intelligent Video Event Analysis And Understanding
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Intelligent Video Event Analysis and Understanding
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.
Intelligent Video Event Analysis and Understanding
Author: Jianguo Zhang
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-01-19
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.
Computer Vision -- ACCV 2010 Workshops
Author: Reinhard Koch
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-09-19
The two-volume set LNCS 6468-6469 contains the carefully selected and reviewed papers presented at the eight workshops that were held in conjunction with the 10th Asian Conference on Computer Vision, in Queenstown, New Zealand, in November 2010. From a total of 167 submissions to all workshops, 89 papers were selected for publication. The contributions are grouped together according to the main workshops topics, which were: computational photography and aesthetics; computer vision in vehicle technology: from Earth to Mars; electronic cultural heritage; subspace based methods; video event categorization, tagging and retrieval; visual surveillance; application of computer vision for mixed and augmented reality.