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Egocentric cameras can be used to benefit such tasks as analyzing fine motor skills, recognizing gestures and learning about hand-object manipulation. To enable such technology , we believe that the hands must detected on the pixel-level to gain important information about the shape of the hands and fingers. We show that the problem of pixel-wise hand(More)
Maximum entropy inverse optimal control (MaxEnt IOC) is an effective means of discovering the underlying cost function of demonstrated human activity and can be used to predict human behavior over low-dimensional state spaces (i.e., forecasting of 2D trajectories). To enable inference in very large state spaces, we introduce an approximate MaxEnt IOC(More)
Our aim is to show how state-of-the-art computer vision techniques can be used to advance prehensile analysis (i.e., understanding the functionality of human hands). Prehen-sile analysis is a broad field of multidisciplinary interest, where researchers painstakingly manually analyze hours of hand-object interaction videos to understand the mechanics of hand(More)
We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any(More)
Generating meaningful digests of videos by extracting interesting frames remains a difficult task. In this paper, we define interesting events as unusual events which occur rarely in the entire video and we propose a novel interesting event summarization framework based on the technique of density ratio estimation recently introduced in machine learning.(More)
We bring together ideas from recent work on feature design for egocentric action recognition under one framework by exploring the use of deep convolutional neural networks (CNN). Recent work has shown that features such as hand appearance, object attributes, local hand motion and camera ego-motion are important for characterizing first-person actions. To(More)
Wearable computing technologies are advancing rapidly and enabling users to easily record daily activities for applications such as life-logging or health monitoring. Recognizing hand and object interactions in these videos will help broaden application domains, but recognizing such interactions automatically remains a difficult task. Activity recognition(More)