Object Tracking by Least Spatiotemporal Searches
This work addresses urban safety management by improving tracking efficiency, but it appears incremental as it builds on existing search strategies.
The paper tackles the problem of tracking objects in urban camera networks with minimal spatiotemporal searches, proposing the IHMs strategy that saves up to one-third of total costs compared to other methods.
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".