A dynamic approach and a new dataset for hand-detection in first person vision

Hand detection and segmentation methods stand as two of the most most prominent objectives in First Person Vision. Their popularity is mainly explained by the importance of a reliable detection and location of the hands to develop human-machine interfaces for emergent wearable cameras. Current developments have been focused on hand segmentation problems, implicitly assuming that hands are always in the field of view of the user. Existing methods are commonly presented with new datasets. However, given their implicit assumption, none of them ensure a proper composition of frames with and without hands, as the hand-detection problem requires. This paper presents a new dataset for hand-detection, carefully designed to guarantee a good balance between positive and negative frames, as well as challenging conditions such as illumination changes, hand occlusions and realistic locations. Additionally, this paper extends a state-of-the-art method using a dynamic filter to improve its detection rate. The improved performance is proposed as a baseline to be used with the dataset.