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no idea, if it will work in your case, but you could try to seperate foreground / background motion like in the paper mentioned above:

            // 3.1 Step 3: Camera/background motion estimation 

            vector<Point2f> foreground;
            // points[0]==prev, points[1]==cur
            Mat H = findHomography(points[0], points[1], RANSAC);
            cerr << H << endl;
            for(size_t i=0; i<points[1].size(); i++)
            {
                Point2f p0 = points[0][i];
                Point2f p1 = points[1][i];
                Mat_<double> col(3, 1);
                col << p0.x, p0.y, 1;
                col = H * col;
                col /= col.at<double>(2);
                double dist = sqrt(pow(col(0) - p1.x, 2) +
                                   pow(col(1) - p1.y, 2));
                if (dist >= 1.5)  // some heuristical threshold value
                {
                    foreground.push_back(p1);
                }
            }
            cerr << "fg " << points[1].size() << " " << foreground.size()  << endl;

no idea, if it will work in your case, but you could try to seperate foreground / background motion like in the paper mentioned above:

            // 3.1 Step 3: Camera/background motion estimation 
            // pointsPrev, pointsCur are vector<Point2f> from LK

            vector<Point2f> foreground;
            // points[0]==prev, points[1]==cur
            Mat H = findHomography(points[0], points[1], findHomography(pointsPrev, pointsCur, RANSAC);
            cerr << H << endl;
            // now, backproject the points, and see, how far off they are:
            for(size_t i=0; i<points[1].size(); i<pointsCur.size(); i++)
            {
                Point2f p0 = points[0][i];
pointsPrev[i];
                Point2f p1 = points[1][i];
pointsCur[i];
                Mat_<double> col(3, 1);
                col << p0.x, p0.y, 1;
                col = H * col;
                col /= col.at<double>(2);
                double dist = sqrt(pow(col(0) - p1.x, 2) +
                                   pow(col(1) - p1.y, 2));
                // small distance == inlier == camera motion
                // large distance == outlier == object motion
                if (dist >= 1.5)  // some heuristical threshold value
                {
                    foreground.push_back(p1);
                }
            }
            cerr << "fg " << points[1].size() pointsCur.size() << " " << foreground.size()  << endl;

no idea, if it will work in your case, but you could try to seperate foreground / background motion like in the paper mentioned above:

            // 3.1 Step 3: Camera/background motion estimation 
            // pointsPrev, pointsCur are vector<Point2f> from LK

            vector<Point2f> foreground;
            Mat H = findHomography(pointsPrev, pointsCur, RANSAC);
            cerr << H << endl;
            // now, backproject the points, and see, how far off they are:
            for(size_t i=0; i<pointsCur.size(); i++)
            {
                Point2f p0 = pointsPrev[i];
                Point2f p1 = pointsCur[i];
                // homogeneous point for mult:
                Mat_<double> col(3, 1);
                col << p0.x, p0.y, 1;
                col = H * col;
                col /= col.at<double>(2);
col(2); // divide by W
                double dist = sqrt(pow(col(0) - p1.x, 2) +
                                   pow(col(1) - p1.y, 2));
                // small distance == inlier == camera motion
                // large distance == outlier == object motion
                if (dist >= 1.5)  // some heuristical threshold value
                {
                    foreground.push_back(p1);
                }
            }
            cerr << "fg " << pointsCur.size() << " " << foreground.size()  << endl;