Traditional Feature Detectors and Trackers use information aggregation in 2D patches to detect and match discriminative patches. However, this information does not remain the same at object boundaries when there is object motion against a significantly varying background. In this paper, we propose a new approach for feature detection, tracking and re-detection that gives significantly improved results at the object boundaries. We utilize level lines or iso-intensity curves that often remain stable and can be reliably detected even at the object boundaries, which they often trace. Stable portions of long level lines are detected and points of high curvature are detected on such curves for corner detection. Further, this level line is used to separate the portions belonging to the two objects, which is then used for robust matching of such points. While such CoMaL (Corners on Maximally-stable Level Line Segments) points were found to be much more reliable at the object boundary regions, they perform comparably at the interior regions as well. This is illustrated in exhaustive experiments on realworld datasets.