In a recent work, we propose the use of motion hints for communicating motion. A motion hint describes motion that is accurate (describes the actual motion) for only a region inside a domain associated with that motion hint; it is the job of the client or decoder to decide the exact region of applicability (ROA). The motion described by a motion hint is invertible and global; that is, it allows the prediction of the ROA associated with a motion hint from any frame that has that hint. Motion hints are applicable to closed-loop prediction, but they are more useful in open-loop prediction scenarios, such as remote browsing of surveillance footage, communicated by a JPIP server, which is the focus of this work. This work proposes a probabilistic multi-scale framework to identifying the ROA; the framework is applicable to multiple overlapping motion hints. The proposed approach is localized (and therefore amenable to parallel processing) and robust to noise, quantization, and changes in contrast. We show that motion hints can be used for real video sequences, and we also present results for the case of three overlapping motion hints (one background and two overlapping foregrounds).