The emergence of new wearable technologies, such as action cameras and smart glasses, has increased the interest of computer vision scientists in the first person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with first person vision (FPV) recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real time, is expected. The current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user–machine interaction, and so on. This paper summarizes the evolution of the state of the art in FPV video analysis between 1997 and 2014, highlighting, among others, the most commonly used features, methods, challenges, and opportunities within the field.

We present a fully unsupervised approach for the discovery of i) task relevant objects and ii) how these objects have been used. Given egocentric video from multiple opera-tors, the approach can discover objects with which the users interact, both static objects such as a coffee machine as well as movable ones such as a cup. Importantly, the com-mon modes of interaction for discovered objects are also found. We investigate using appearance, position, motion and attention, and present results using each and a combi-nation of relevant features. Results show that the method is capable of discovering 95% of task relevant objects on a variety of daily tasks such as initialising a printer, preparing a coffee and setting up a gym machine. In addition, the approach enables the automatic generation of guidance video on how these objects have been used before. 1

Abstract. Hand detection and segmentation methods stand as two of the most most prominent objectives in First Person Vision. Their popularity is mainly ex-plained by the importance of a reliable detection and location of the hands to develop human-machine interfaces for emergent wearable cameras. Current de-velopments have been focused on hand segmentation problems, implicitly as-suming that hands are always in the field of view of the user. Existing methods are commonly presented with new datasets. However, given their implicit as-sumption, 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 pos-itive 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. 1