TY - GEN
T1 - Spatio-temporal volume-based shape modelling for video event detection
AU - Wang, Jing
AU - Xu, Zhijie
AU - Liu, Ying
PY - 2013
Y1 - 2013
N2 - In a typical computer vision application, such as video event detection, the 'meaningful' information is fundamentally represented by pre-defined features, which determine the appropriate analytical methodologies in the following processing phases. Based on the uncompressed low-level image characteristics, such as colour, intensity and spatial positions, the features used for event detection in this research are predominantly based on 3D shapes, regional textures, and sudden colour/intensity. In this research, a spatio-temporal volume-based shape feature extraction and modelling approach has been proposed. This method starts from defining video data as 3D volumetric shapes by using active contour (AC) segmentation techniques. Based on the nature of its 3D distribution, a dynamic windowing mechanism has been developed for improving the segmentation performance when deploying the AC algorithm. The runtime performance of the prototype system has been evaluated which validated the design and its potential in improving volume-based event recognition.
AB - In a typical computer vision application, such as video event detection, the 'meaningful' information is fundamentally represented by pre-defined features, which determine the appropriate analytical methodologies in the following processing phases. Based on the uncompressed low-level image characteristics, such as colour, intensity and spatial positions, the features used for event detection in this research are predominantly based on 3D shapes, regional textures, and sudden colour/intensity. In this research, a spatio-temporal volume-based shape feature extraction and modelling approach has been proposed. This method starts from defining video data as 3D volumetric shapes by using active contour (AC) segmentation techniques. Based on the nature of its 3D distribution, a dynamic windowing mechanism has been developed for improving the segmentation performance when deploying the AC algorithm. The runtime performance of the prototype system has been evaluated which validated the design and its potential in improving volume-based event recognition.
KW - event detection
KW - feature extraction
KW - shape
KW - spatio-temporal volume
UR - http://www.scopus.com/inward/record.url?scp=84892712870&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:84892712870
SN - 9781908549082
T3 - ICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing: Future Energy and Automation
SP - 185
EP - 190
BT - ICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing
PB - IEEE Computer Society
T2 - 19th International Conference on Automation and Computing, ICAC 2013
Y2 - 13 September 2013 through 14 September 2013
ER -