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".
@article{arxiv.2007.09288,
title = {Object Tracking by Least Spatiotemporal Searches},
author = {Zhiyong Yu and Lei Han and Chao Chen and Wenzhong Guo and Zhiwen Yu},
journal= {arXiv preprint arXiv:2007.09288},
year = {2021}
}