Integrating Vision and Language: Semantic Description of Traffic Events from Image Sequences
We propose an event extraction method from traffic image seque-nces. This method extracts moving objects and their trajectories from image sequences recorded by a stationary camera. These trajectories are mapped to 3D virtual space and physical parameters such as velocity and direction are estimated. After that, traffic events are extracted from these trajectories and physical parameters based on case-frame analysis in the field of natural language processing. Our method facilitates to describe events easily and detect general traffic events and abnormal situations. The experimental results of actual intersection traffic image sequence have shown the effectiveness of the method.
KeywordsNatural Language Processing Semantic Category Semantic Description Knowledge Database Stationary Camera
Unable to display preview. Download preview PDF.
- 4.Herzog, G., Rohr, K.: Integrating vision and language: Towards automatic description of human movements. In: Proc. 19th Annual German Conf. on Artificial Intelligence, pp. 257–268 (1995)Google Scholar
- 6.Kojima, A., Tahara, N., Tamura, T., Fukunaga, K.: Natural Language Description of Human Behavior from Image Sequences. IEICE J81-D-II(8), 1867–1875 (1998) (in Japanese)Google Scholar
- 7.Porikli, F., Tuzel, O.: Bayesian Background Modeling for Foreground Detection. In: ACM International Workshop on Video Surveillance and Sensor Networks (VSSN), pp. 55–28 (November 2005)Google Scholar
- 8.Tuzel, O., Porikli, F., Meer, P.: A Bayesian Approach to Background Modeling. In: IEEE Workshop on Machine Vision for Intelligent Vehicles (MVIV), vol. 3, p. 58 (June 2005)Google Scholar
- 9.Porikli, F., Tuzel, O.: Multi-Kernel Object Tracking. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1234–1237 (2005)Google Scholar
- 10.Fillmore, C.J.: The case for case. In: Bach, E., Harms, R. (eds.) Universals in Linguistic Theory. Rinehart and Wiston (1968)Google Scholar