Multi-Object Events Recognition from Video Sequences using Extended Finite State Machine
Representation and recognition of human activities or other events generated by vehicles in restricted settings such as airports, railway station and parking lots is of great importance in automated surveillance systems. The problem is difficult because: 1) at the lower level, detection of primitive actions is inaccurate due to changes in illumination, noise and resolution of video camera. 2) at the higher level, activities or events in such settings are usually originated by multi-object. It is hard to represent and parse the complex activities and events. In this paper, we describe a new approach for representing and recognizing the events in video sequences, and the approach is able to handle complex multi-agent interactions. Our experimental results on a dataset that consisting of videos took by normal camera and videos took by surveillance camera in laboratory.
Multi-object events recognition extended finite state machine human activities recognition
Chun Yuan Wei Xu
Department of Computer Science and Technology Graduate School at Shenzhen, Tsinghua University Shenz Department of Computer Science andTechnology Graduate School at Shenzhen, Tsinghua University Shenz
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
207-210
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）