Abstract:
We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their ...Show MoreMetadata
Abstract:
We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently, regions where estimations have high certainty are decomposed into zones by superpixel segmentation. Each zone represents a region where motions of entities can be explained by a quasilinear dynamical model. We evaluated the proposed method with two datasets and confirmed its reliability for characterizing and classifying trajectories.
Published in: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Date of Conference: 29 August 2017 - 01 September 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information: