论文标题
匿名视频分析的基准
Benchmark for Anonymous Video Analytics
论文作者
论文摘要
户外观众的测量旨在计算和表征在物理世界中接触广告内容的人们。尽管基于计算机视觉的受众测量解决方案引起了人们的兴趣，但没有普遍接受的基准来评估和比较其性能。在本文中，我们提出了第一个用于数字室外受众群体测量值的基准，该基准评估了受众本地化和计数的基于视觉的任务以及受众人口统计。基准由在多个位置捕获的小说，数据集和一组绩效指标组成。使用基准测试，我们对四个硬件平台上的八种开源算法进行了深入的比较，该算法以及GPU和CPU优化的推论以及两种用于本地化，计数，年龄和性别估算的商业现成解决方案。该基准和相关的开源代码可在http://ava.eecs.qmul.ac.uk上找到。
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.
