# Bandwidth limited object recognition in high resolution imagery

## TL;DR

This paper introduces a bandwidth-efficient object recognition method in high-resolution images that selectively requests high-resolution data, significantly reducing bandwidth use with minimal impact on accuracy.

## Contribution

It presents two novel active information seeking models for object detection that optimize bandwidth by focusing on promising regions in low-resolution imagery.

## Key findings

- System saves up to ten times bandwidth compared to JPEG compression.
- Recognition performance remains high despite reduced bandwidth.
- Models effectively identify regions of interest for high-resolution analysis.

## Abstract

This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.