Non-rigid-motion recognition using a moving mono camera

The focus of this contribution is the detection of moving objects and the classification of their motion as rigid or non-rigid. A new processing approach for analyzing video data of a moving monocular camera is introduced. It is mainly based on the evaluation of the optical flow in two different processing steps: detection and classification. First, the ego motion is evaluated in order to separate moving objects from the background clearly. The implemented corresponding algorithm features both, the extraction of wrongly estimated displacement vectors and the ego motion compensation. The processing step applied after that deals with the classification of the detected objects. It will be shown that a reliable distinction between rigid and non-rigid objects is well realizable using some features derived from the motion orientation histogram. For a corresponding evaluation, the case of the pedestrian recognition was taken into account.