Video-based tracking of small targets in a dense environment of clutter is very difficult, because the image resolution of the target is too low to provide reliable information for matching, and in turn the clutter generates a large number of false positive matches and distractions. Most traditional methods attempt to oppose the target to the environment, and are thus confronted in handling the enormous distractions. In fact, a target is rarely isolated and independent to the environment, e.g., when persistent disturbances are present in the vicinity of the target. Therefore, there may exist some objects that exhibit short-term or even longer-term motion correlation to the target. They constitute a very useful spatial contexts of the target. Thus, taking the advantage of the contextual information in an efficient way can improve the robustness of target tracking, as the spatial contexts provide extra constraints in target matching and additional verification in data association. This paper presents a new approach of context-aware tracking for small targets, in which a set of motion-correlated auxiliary objects are automatically discovered on-the-fly. The image region of one such auxiliary object generates a specific spatial context of the target, and leads to an individual contextual constraint to the motion of the target. Under the small motion assumption on two consecutive frames, these individual contextual constraints have linear forms. The collection of all such individual contextual constraints gives a contextual system, based on which the target motion can be accurately estimated so that the association of the target over consecutive image frames can be reliably constructed. This new approach is computationally efficient. Extensive experiments on real test video sequences show the effectiveness and efficiency of the proposed approach.