A dynamic object filtering approach based on object detection and geometric constraint between frames

Abstract

In order to eliminate the influence of moving targets in visual positioning, a dynamic object filtering approach based on object detection and inter-frame geometric constraints is proposed to filter the dynamic objects in monocular images. The object detection algorithm is firstly used to identify and locate objects in the single-frame image, and the object matching between frames is performed. Then, the depth estimation network and pose recovery network are trained jointly to output estimated depth along with transformation matrix of object centroids between frames respectively. Finally, the mapping centroid is obtained from the estimated depth and inter-frame transformation matrix. The joint constraint function is performed to complete the detection and filtering of dynamic objects. The experimental results show that the proposed dynamic object filtering approach not only can filter moving objects in sequence images accurately but also allows to reserve the dynamic objects that are temporarily in the stop state. The generalization ability of this approach is also verified in a real urban road scene and meets the requirements of followup research in visual positioning.