Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on computer vision are of increasing interest, no commonly accepted benchmark exists to evaluate and compare their performance. In this paper, we propose the first benchmark for digital out-of-home audience measurement that evaluates the vision-based tasks of audience localization and counting, and audience demographics. The benchmark is composed of a novel, dataset captured at multiple locations and a set of performance measures. Using the benchmark, we present an in-depth comparison of eight open-source algorithms on four hardware platforms with GPU and CPU-optimized inferences and of two commercial off-the-shelf solutions for localization, count, age, and gender estimation. This benchmark and related open-source codes are available at http://ava.eecs.qmul.ac.uk.

翻译：虽然基于计算机视野的受众计量解决方案越来越令人感兴趣,但没有共同接受的基准来评估和比较其业绩。在本文件中,我们提出了评估基于视觉的受众本地化和计数任务以及受众人口统计的数码外受众计量的第一个基准。该基准包括一个新颖的、在多个地点收集的数据集和一套业绩计量。我们利用该基准,对四个硬件平台上八种公开源码算法进行了深入比较,这些算法包括GPU和CPU优化的推论,以及两个商业现成的本地化、计数、年龄和性别估计解决方案。这一基准和相关的开放源代码可在http://ava.eecs.qmul.ac.uk上查阅。