  Tracking a car or a person in a city is crucial for urban safety management.
How can we complete the task with minimal number of spatiotemporal searches
from massive camera records? This paper proposes a strategy named IHMs
(Intermediate Searching at Heuristic Moments): each step we figure out which
moment is the best to search according to a heuristic indicator, then at that
moment search locations one by one in descending order of predicted appearing
probabilities, until a search hits; iterate this step until we get the object's
current location. Five searching strategies are compared in experiments, and
IHMs is validated to be most efficient, which can save up to 1/3 total costs.
This result provides an evidence that "searching at intermediate moments can
save cost".
