Computer vision and pattern recognition technologies will play an important improvement in audience measurement for its capability to understand several visual cues such as demographics, free gaze estimation, dwell time, emotion and group people proxemics, where low spatial resolution of acquired subjects, changing of the pose, occlusion, illumination changes, large variability of intra-class female age and ethnicity cohorts represent some critical aspects for recognition.
- Dwell time estimation
- Gender recognition
- Age and Age group estimation
- Behaviour analysis
- People counting in multi-camera network
- People recurrence in long time window
- Ethnicity recognition
- Emotion analysis
- Free eye gaze estimation
- Group of related people: detection, tracking and behaviour analysis
- Path optimization and queue management in the point of sale
- Privacy preserving in audience measurement
- Modelling consumer behaviour
- Annotated Dataset proposal
- BigData analytics for audience measurement
- Multimodal approaches for audience measurement
- Other visual cues for customer profiling