Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision

Abstract

We present a quantitative study of digital signage audience measurement using computer vision. We developed a camera-enhanced digital signage display that acquires audience measurement metrics with computer vision algorithms. Temporal metrics of a person’s dwell time, display in-view time and attention time are extracted. The system also determines demographic metrics of the gender and age group. The digital signage display was deployed in a real-world environment of a clothing boutique, where demographic and viewership data of 1294 store customers were recorded, manually verified and analysed. The analysis shows that 35% of customers specifically looked-at the display, having the average attention time of 0.7 s. Interestingly, the attention time was substantially higher for men (1.2 s) than for women (0.4 s). Age group comparison reveals that children (1–14 years) are the most responsive to the digital signage. Finally, the analysis shows that the average attention time is significantly higher when displaying the dynamic content (0.9 s) when compared with the static content (0.6 s).