Semi-automatic Hand Detection: A case study on real life mobile eye-tracker data

Abstract

In this paper we present a highly accurate algorithm for the detection of human hands in real-life 2D image sequences. Current state of the art algorithms show relatively poor detection accuracy results on unconstrained, challenging images. To overcome this, we introduce a detection scheme in which we combine several well known detection techniques combined with an advanced elimination mechanism to reduce false detections. Furthermore we present a novel (semi-)automatic framework achieving detection rates up to 100%, with only minimal manual input. This is a useful tool in supervised applications where an error-free detection result is required at the cost of a limited amount of manual effort. As an application, this paper focuses on the analysis of video data of human-human interaction, collected with the scene camera of mobile eye-tracking glasses. This type of data is typically annotated manually for relevant features (e.g. visual fixations on gestures), which is a time-consuming, tedious and error-prone task. The usage of our semi-automatic approach reduces the amount of manual analysis dramatically. We also present a new fully annotated benchmark dataset on this application which we made publicly available.De Beugher S., Brône G., Goedemé T., ''Semi-automatic hand detection: A case study on real life mobile eye-tracker data'', Proceedings 10th international conference on computer vision theory and applications - VISAPP 2015, vol. 2, pp. 121-129, March 11-14, 2015, Berlin, Germany. no issnstatus: publishe