A Multimedia Data Mining Framework: Mining Information from Traffic Video Sequences
Journal of Intelligent Information Systems
Expert Systems with Applications: An International Journal
Vehicle tracking and traffic parameter extraction based on discrete wavelet transform
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hi-index | 0.00 |
Abstract: S.C. Chen et al. (1999) proposed a multimedia augmented transition network (ATN) model, together with its multimedia input strings, to model and structure video data. This multimedia ATN model was based on an ATN model that had been used within the artificial intelligence (AI) arena for natural-language understanding systems, and its inputs were modeled by multimedia input strings. The temporal and spatial relations of semantic objects were captured by an unsupervised video segmentation method called the SPCPE (simultaneous partitioning and class parameter estimation) algorithm, and they were modeled by the multimedia input strings. However, the segmentation method used was not able to identify objects that are overlapped together within video frames. The identification of overlapped objects is a great challenge. For this purpose, a backtrack-chain-update-split algorithm is developed in this paper that identifies the split segment (object) and uses this information in the current frame to update the previous frames in a backtrack-chain manner. The proposed split algorithm provides more accurate temporal and spatial information of the semantic objects for video indexing.