Moving Object Detection Through Robust Matrix Completion Augmented With Objectness

Abstract

We present a novel approach for unsupervised detection of moving objects with nonsalient movements (e.g., rodents in their home cage). The proposed approach starts with separating the moving object from its background by modeling the background in a computationally efficient way. The background modeling is based on the assumption that background in natural videos lies on a low-dimensional subspace. We formulated and solved this problem using a low-rank matrix completion framework. To achieve computational efficiency, we proposed the fast robust matrix completion (fRMC) algorithm, which benefits from the in-face extended Frank–Wolfe approach as its optimization solver. We then augmented our fRMC-based moving object detection by incorporating the spatial information of the object as its objectness into the detection algorithm. With this augmentation we tackle the problem of nonsalient motion. The proposed fRMC algorithm is evaluated on background models challenge and Stuttgart artificial background subtraction datasets. Its detection results are then compared with the popular methods of background subtraction based on the robust principle component analysis and low-rank robust matrix completion methods, solved by inexact augmented Lagrangian multiplier and fast principal component pursuit via alternating minimization (FPCP). The outcomes showed faster computation, at least twice as when other methods are applied, while having a comparable detection accuracy. Moreover, fRMC observed to outperform the FPCP algorithm in background/foreground separation with minor computational overhead. Beyond that, we verified the performance improvement of the augmented fRMC with objectness on detecting the nonsalient motion of in-cage mice using the Caltech resident-intruder mice dataset. The evaluation showed 10% improvement in the detection performance, while significantly dropping the computational time.