Object Tracking by Least Spatiotemporal Searches

TL;DR

This paper introduces IHMs, a heuristic-based strategy for efficient object tracking in city surveillance, significantly reducing search costs by intelligently selecting search moments and locations.

Contribution

The paper presents a novel heuristic strategy, IHMs, that optimizes spatiotemporal searches for object tracking, outperforming existing methods in efficiency.

Findings

IHMs saves up to one-third of total search costs.

IHMs outperforms five other search strategies in experiments.

Searching at intermediate moments reduces overall tracking costs.

Abstract

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".