In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotion) and salient local motion observed in videos. We describe a representation framework based on time series pooling, which is designed to abstract short-term long-term changes in feature descriptor elements. The idea is to keep track of how descriptor values are changing over time and summarize them to represent motion in the activity video. The framework is general, handling any types of per-frame feature descriptors including conventional motion descriptors like histogram of optical flows (HOF) as well as appearance descriptors from more recent convolutional neural networks (CNN). We experimentally confirm that our approach clearly outperforms previous feature representations including bag-of-visual-words and improved Fisher vector (IFV) when using identical underlying feature descriptors. We also confirm that our feature representation has superior performance to existing state-of-the-art features like local spatio-temporal features and Improved Trajectory Features (originally developed for 3rd-person videos) when handling first-person videos. Multiple first-person activity datasets were tested under various settings to confirm these findings.
Recognition from first-person videos is a topic with an increasing amount of attention. There are works focusing on first-person-specific features, including hand locations in first-person videos @cite_15 and human gaze estimation based on first-person videos @cite_16 . There also have been works on object recognition from first-person videos @cite_17 @cite_12 .
@cite_15 @cite_16 @cite_12 @cite_17
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First-person videos have unique characteristics such as heavy egocentric motion, strong preceding events, salient transitional activities and post-event impacts. Action recognition methods designed for third person videos may not optimally represent actions captured by first-person videos. We propose a method to represent the high level dynamics of sub-events in first-person videos by dynamically pooling features of sub-intervals of time series using a temporal feature pooling function. The sub-event dynamics are then temporally aligned to make a new series. To keep track of how the sub-event dynamics evolve over time, we recursively employ the Fast Fourier Transform on a pyramidal temporal structure. The Fourier coefficients of the segment define the overall video representation. We perform experiments on two existing benchmark first-person video datasets which have been captured in a controlled environment. Addressing this gap, we introduce a new dataset collected from YouTube which has a larger number of classes and a greater diversity of capture conditions thereby more closely depicting real-world challenges in first-person video analysis. We compare our method to state-of-the-art first person and generic video recognition algorithms. Our method consistently outperforms the nearest competitors by 10.3 , 3.3 and 11.7 respectively on the three datasets. This paper presents a method for the detection and recognition of social interactions in a day-long first-person video of u social event, like a trip to an amusement park. The location and orientation of faces are estimated and used to compute the line of sight for each face. The context provided by all the faces in a frame is used to convert the lines of sight into locations in space to which individuals attend. Further, individuals are assigned roles based on their patterns of attention. The rotes and locations of individuals are analyzed over time to detect and recognize the types of social interactions. In addition to patterns of face locations and attention, the head movements of the first-person can provide additional useful cues as to their attentional focus. We demonstrate encouraging results on detection and recognition of social interactions in first-person videos captured from multiple days of experience in amusement parks. First-person action recognition is a recent problem in computer vision, where an observer wears body cameras to understand and recognize actions from the captured video sequences. Technological advances have made it possible to offer small wearable cameras that can be attached onto bike helmets, belts, animal halters, among other accessories. Examples of potential applications include sports, security, healthcare, visual lifelogging, among others. In this paper, we propose a novel approach to first-person action recognition that consists in encoding video appearance, shape and motion information as visual rhythms and describing them through texture analysis. Experiments are conducted on the DogCentric Activity and JPL First-Person Interaction datasets, showing accuracy improvement over the baselines. We focus on the problem of wearer's action recognition in first person a.k.a. egocentric videos. This problem is more challenging than third person activity recognition due to unavailability of wearer's pose and sharp movements in the videos caused by the natural head motion of the wearer. Carefully crafted features based on hands and objects cues for the problem have been shown to be successful for limited targeted datasets. We propose convolutional neural networks (CNNs) for end to end learning and classification of wearer's actions. The proposed network makes use of egocentric cues by capturing hand pose, head motion and saliency map. It is compact. It can also be trained from relatively small number of labeled egocentric videos that are available. We show that the proposed network can generalize and give state of the art performance on various disparate egocentric action datasets.