Modeling of moving object trajectory by spatio-temporal learning for abnormal behavior detection
2011pp. 119–123
Citations Over TimeTop 11% of 2011 papers
Abstract
This paper proposes a trajectory analysis method by handling the spatio-temporal property of trajectory. Not using similarity measures of two trajectories, our model analyzes overall path of a trajectory. Learning of spatio property is presented as semantic regions (e.g. go straight, turn left, turn right) that are clustered effectively using topic model. The temporal order of observations on a trajectory is taken into account using HMM for detecting global anomaly. Results of experiments show that modeling of semantic region and detecting of unusual trajectories are successful even in complex scenes.
Related Papers
- → An Object Detection and Pose Estimation Approach for Position Based Visual Servoing(2017)5 cited
- → Tracking in 3D: Image Variability Decomposition for Recovering Object Pose and Illumination(1999)15 cited
- → Foreground object segmentation from binocular stereo video(2005)2 cited
- → Object-oriented stripe structured-light vision-guided robot(2017)2 cited
- → 6-DOF object localization by combining monocular vision and robot arm kinematics(2017)1 cited