On a minute-to-minute basis people undergo numerous fluid interactions with objects that barely register on a conscious level. Recent neuroscientific research demonstrates that humans have a fixed size prior for salient objects. This suggests that a salient object in 3D undergoes a consistent transformation such that people's visual system perceives it with an approximately fixed size. This finding indicates that there exists a consistent egocentric object prior that can be characterized by shape, size, depth, and location in the first person view. In this paper, we develop an EgoObject Representation, which encodes these characteristics by incorporating shape, location, size and depth features from an egocentric RGBD image. We empirically show that this representation can accurately characterize the egocentric object prior by testing it on an egocentric RGBD dataset for three tasks: the 3D saliency detection, future saliency prediction, and interaction classification. This representation is evaluated on our new Egocentric RGBD Saliency dataset that includes various activities such as cooking, dining, and shopping. By using our EgoObject representation, we outperform previously proposed models for saliency detection (relative 30 improvement for 3D saliency detection task) on our dataset. Additionally, we demonstrate that this representation allows us to predict future salient objects based on the gaze cue and classify people's interactions with objects.
In the recent work, several methods employed egocentric (first-person view) cameras for the tasks such as video summarization @cite_25 @cite_26 , video stabilization @cite_11 , object recognition @cite_24 @cite_0 , and action and activity recognition @cite_21 @cite_6 @cite_18 @cite_10 .
@cite_18 @cite_26 @cite_21 @cite_6 @cite_24 @cite_0 @cite_10 @cite_25 @cite_11
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Recent technological advances have made lightweight, head mounted cameras both practical and affordable and products like Google Glass show first approaches to introduce the idea of egocentric (first-person) video to the mainstream. Interestingly, the computer vision community has only recently started to explore this new domain of egocentric vision, where research can roughly be categorized into three areas: Object recognition, activity detection recognition, video summarization. In this paper, we try to give a broad overview about the different problems that have been addressed and collect and compare evaluation results. Moreover, along with the emergence of this new domain came the introduction of numerous new and versatile benchmark datasets, which we summarize and compare as well. The introduction of wearable video cameras (e.g., GoPro) in the consumer market has promoted video life-logging, motivating users to generate large amounts of video data. This increasing flow of first-person video has led to a growing need for automatic video summarization adapted to the characteristics and applications of egocentric video. With this paper, we provide the first comprehensive survey of the techniques used specifically to summarize egocentric videos. We present a framework for first-person view summarization and compare the segmentation methods and selection algorithms used by the related work in the literature. Next, we describe the existing egocentric video datasets suitable for summarization and, then, the various evaluation methods. Finally, we analyze the challenges and opportunities in the field and propose new lines of research. We address the challenging problem of recognizing the camera wearer's actions from videos captured by an egocentric camera. Egocentric videos encode a rich set of signals regarding the camera wearer, including head movement, hand pose and gaze information. We propose to utilize these mid-level egocentric cues for egocentric action recognition. We present a novel set of egocentric features and show how they can be combined with motion and object features. The result is a compact representation with superior performance. In addition, we provide the first systematic evaluation of motion, object and egocentric cues in egocentric action recognition. Our benchmark leads to several surprising findings. These findings uncover the best practices for egocentric actions, with a significant performance boost over all previous state-of-the-art methods on three publicly available datasets. Identifying handled objects, i.e. objects being manipulated by a user, is essential for recognizing the person's activities. An egocentric camera as worn on the body enjoys many advantages such as having a natural first-person view and not needing to instrument the environment. It is also a challenging setting, where background clutter is known to be a major source of problems and is difficult to handle with the camera constantly and arbitrarily moving. In this work we develop a bottom-up motion-based approach to robustly segment out foreground objects in egocentric video and show that it greatly improves object recognition accuracy. Our key insight is that egocentric video of object manipulation is a special domain and many domain-specific cues can readily help. We compute dense optical flow and fit it into multiple affine layers. We then use a max-margin classifier to combine motion with empirical knowledge of object location and background movement as well as temporal cues of support region and color appearance. We evaluate our segmentation algorithm on the large Intel Egocentric Object Recognition dataset with 42 objects and 100K frames. We show that, when combined with temporal integration, figure-ground segmentation improves the accuracy of a SIFT-based recognition system from 33 to 60 , and that of a latent-HOG system from 64 to 86 . We present a method to analyze daily activities, such as meal preparation, using video from an egocentric camera. Our method performs inference about activities, actions, hands, and objects. Daily activities are a challenging domain for activity recognition which are well-suited to an egocentric approach. In contrast to previous activity recognition methods, our approach does not require pre-trained detectors for objects and hands. Instead we demonstrate the ability to learn a hierarchical model of an activity by exploiting the consistent appearance of objects, hands, and actions that results from the egocentric context. We show that joint modeling of activities, actions, and objects leads to superior performance in comparison to the case where they are considered independently. We introduce a novel representation of actions based on object-hand interactions and experimentally demonstrate the superior performance of our representation in comparison to standard activity representations such as bag of words. We present a video summarization approach for egocentric or “wearable” camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer's day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video — such as the nearness to hands, gaze, and frequency of occurrence — and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance of objects and people that have never been seen previously. Our results with 17 hours of egocentric data show the method's promise relative to existing techniques for saliency and summarization. Egocentric cameras are becoming increasingly popular and provide us with large amounts of videos, captured from the first person perspective. At the same time, surveillance cameras and drones offer an abundance of visual information, often captured from top-view. Although these two sources of information have been separately studied in the past, they have not been collectively studied and related. Having a set of egocentric cameras and a top-view camera capturing the same area, we propose a framework to identify the egocentric viewers in the top-view video. We utilize two types of features for our assignment procedure. Unary features encode what a viewer (seen from top-view or recording an egocentric video) visually experiences over time. Pairwise features encode the relationship between the visual content of a pair of viewers. Modeling each view (egocentric or top) by a graph, the assignment process is formulated as spectral graph matching. Evaluating our method over a dataset of 50 top-view and 188 egocentric videos taken in different scenarios demonstrates the efficiency of the proposed approach in assigning egocentric viewers to identities present in top-view camera. We also study the effect of different parameters such as the number of egocentric viewers and visual features. 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. The use of wearable cameras makes it possible to record life logging egocentric videos. Browsing such long unstructured videos is time consuming and tedious. Segmentation into meaningful chapters is an important first step towards adding structure to egocentric videos, enabling efficient browsing, indexing and summarization of the long videos. Two sources of information for video segmentation are (i) the motion of the camera wearer, and (ii) the objects and activities recorded in the video. In this paper we address the motion cues for video segmentation. Motion based segmentation is especially difficult in egocentric videos when the camera is constantly moving due to natural head movement of the wearer. We propose a robust temporal segmentation of egocentric videos into a hierarchy of motion classes using a new Cumulative Displacement Curves. Unlike instantaneous motion vectors, segmentation using integrated motion vectors performs well even in dynamic and crowded scenes. No assumptions are made on the underlying scene structure and the method works in indoor as well as outdoor situations. We demonstrate the effectiveness of our approach using publicly available videos as well as choreographed videos. We also suggest an approach to detect the fixation of wearer's gaze in the walking portion of the egocentric videos.