Joint detection and tracking of independently moving objects in stereo sequences using scale-invariant feature transform features and particle filter

Abstract

A scale-invariant feature transform (SIFT)-based particle filter algorithm is presented for joint detection and tracking of independently moving objects in stereo sequences observed by uncalibrated moving cameras. The major steps include feature detection and matching, moving object detection based on multiview geometric constraints, and tracking based on particle filter. Our contributions are first, a novel closed-loop mapping (CLM) multiview matching scheme proposed for stereo matching and motion tracking. CLM outperforms several state-of-the-art SIFT matching methods in terms of density and reliability of feature correspondences. Our second contribution is a multiview epipolar constraint derived from the relative camera positions in pairs of consecutive stereo views for independent motion detection. The multiview epipolar constraint is able to detect moving objects followed by moving cameras in the same direction, a configuration where the epipolar constraint fails. Our third contribution is a proposed dimensional variable particle filter for joint detection and tracking of independently moving objects. Multiple moving objects entering or leaving the field of view are handled effectively within the proposed framework. Experimental results on real-world stereo sequences demonstrate the effectiveness and robustness of our method.