Object-Level Motion Detection From Moving Cameras

It is important for a moving observer to be able to identify his/her surrounding objects and determine whether these objects are moving or stationary, which is called object-level motion detection. Detecting object-level motion from moving cameras is a difficult problem to solve for collision-free navigation due to the dual motion introduced by the mixture of the camera motion and the object motion. This paper presents a novel technique that detects object-level motion from a freely moving camera using only two consecutive video frames. A context-aware motion descriptor (CMD) is designed based on the object’s moving speed and moving direction relative to that of the moving camera. The CMD employs the contextual information, e.g., the optical flow of the image background surrounding the moving object of interest, which describes the object motion behavior better than other contexts such as the camera’s GPS and direction. The inconsistency between the histogram of oriented optical flow of the object and its surrounding background is measured for the object-level motion detection. The proposed technique has been evaluated over two types of widely studied objects, i.e., vehicles and humans that are captured with different sizes, moving speeds, and image backgrounds using a moving camera. Experiments on challenging real-world videos show promising performance in object-level motion detection.