Egocentric, or first-person vision which became popular in recent years with an emerge in wearable technology, is different than exocentric (third-person) vision in some distinguishable ways, one of which being that the camera wearer is generally not visible in the video frames. Recent work has been done on action and object recognition in egocentric videos, as well as work on biometric extraction from first-person videos. Height estimation can be a useful feature for both soft-biometrics and object tracking. Here, we propose a method of estimating the height of an egocentric camera without any calibration or reference points. We used both traditional computer vision approaches and deep learning in order to determine the visual cues that results in best height estimation. Here, we introduce a framework inspired by two stream networks comprising of two Convolutional Neural Networks, one based on spatial information, and one based on information given by optical flow in a frame. Given an egocentric video as an input to the framework, our model yields a height estimate as an output. We also incorporate late fusion to learn a combination of temporal and spatial cues. Comparing our model with other methods we used as baselines, we achieve height estimates for videos with a Mean Average Error of 14.04 cm over a range of 103 cm of data, and classification accuracy for relative height (tall, medium or short) up to 93.75 where chance level is 33 .
With increasing popularity of egocentric vision in recent years, much research work is being focused to solve computer vision problems with the perspective of first person. Egocentric or First Person Vision problems are unlike classic computer vision problems since the person whose actions are being recorded is not captured. Egocentric vision poses unique challenges like non-static cameras, unusual view points, motion blur @cite_2 , variations in illumination with the varying positions of camera wearer, real time video analysis requirements, etcetera @cite_8 . Tan @cite_26 demonstrate that challenges posed by egocentric vision can be handled in a more efficient manner if analyzed differently than exocentric. Much work has been done to address classic computer vision problems, now with egocentric perspective such as objects understanding @cite_28 @cite_29 , object detection @cite_20 @cite_18 @cite_2 , object tracking @cite_3 @cite_22 , and activity recognition @cite_24 @cite_4 @cite_15 @cite_11 .
@cite_18 @cite_26 @cite_4 @cite_22 @cite_8 @cite_28 @cite_29 @cite_3 @cite_24 @cite_2 @cite_15 @cite_20 @cite_11
48480781 2106640360 1947050545 1410291062 2025581566 2434184923 2337242548 854053868 2204609240 2081490348 2167626157
We focus on the task of hand pose estimation from egocentric viewpoints. For this problem specification, we show that depth sensors are particularly informative for extracting near-field interactions of the camera wearer with his her environment. Despite the recent advances in full-body pose estimation using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D images is still an unsolved problem. The problem is exacerbated when considering a wearable sensor and a first-person camera viewpoint: the occlusions inherent to the particular camera view and the limitations in terms of field of view make the problem even more difficult. We propose to use task and viewpoint specific synthetic training exemplars in a discriminative detection framework. We also exploit the depth features for a sparser and faster detection. We evaluate our approach on a real-world annotated dataset and propose a novel annotation technique for accurate 3D hand labelling even in case of partial occlusions. First-person view (FPV) video data is set to proliferate rapidly, due to many consumer wearable-camera devices coming onto the market. Research into FPV (or "egocentric") vision is also becoming more common in the computer vision community. However, it is still unclear what the fundamental characteristics of such data are. How is it really different from third-person view (TPV) data? Can all FPV data be treated the same? In this first attempt to approach these questions in a quantitative and empirical manner, we analyzed a meta-collection of 21 FPV and TPV datasets totaling more than 165 hours of video. We performed the first quantitative characterization of FPV videos over multiple datasets, encompassing virtually all available FPV datasets. Validating this characterization, linear classifiers trained on low-level features to perform FPV-versus-TPV classification achieved good baseline performance. Accuracy peaked at 81 for 2-minute clips, but 67 accuracy was achieved even with 1-second clips. Our low-level features are fast to compute and do not require annotation. Overall, our work uncovered insights regarding the basic nature and characteristics of FPV data. 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. A method for multiface tracking in low frame rate egocentric videos is proposed (eBoT).eBoT generates a tracklet for each detected face and groups similar tracklets.eBoT extracts a prototype for each group of tracklets and estimates its confidence.eBoT is robust to drastic changes in location and appearance of faces.eBoT is robust to partial and severe occlusions and is able to localize them. Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. Egocentric cameras are becoming more popular, introducing increasing volumes of video in which the biases and framing of traditional photography are replaced with those of natural viewing tendencies. This paradigm enables new applications, including novel studies of social interaction and human development. Recent work has focused on identifying the camera wearer's hands as a first step towards more complex analysis. In this paper, we study how to disambiguate and track not only the observer's hands but also those of social partners. We present a probabilistic framework for modeling paired interactions that incorporates the spatial, temporal, and appearance constraints inherent in egocentric video. We test our approach on a dataset of over 30 minutes of video from six pairs of subjects. Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly ad dress these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL). The way people look in terms of facial attributes (ethnicity, hair color, facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat, hoodies, etc.) is highly dependent on geo-location and weather condition, respectively. This work explores, for the first time, the use of this contextual information, as people with wearable cameras walk across different neighborhoods of a city, in order to learn a rich feature representation for facial attribute classification, without the costly manual annotation required by previous methods. By tracking the faces of casual walkers on more than 40 hours of egocentric video, we are able to cover tens of thousands of different identities and automatically extract nearly 5 million pairs of images connected by or from different face tracks, along with their weather and location context, under pose and lighting variations. These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features. Finally, the network is fine-tuned with manually annotated samples. We perform an extensive experimental analysis on wearable data and two standard benchmark datasets based on web images (LFWA and CelebA). Our method outperforms by a large margin a network trained from scratch. Moreover, even without using manually annotated identity labels for pre-training as in previous methods, our approach achieves results that are better than the state of the art. While egocentric video is becoming increasingly popular, browsing it is very difficult. In this paper we present a compact 3D Convolutional Neural Network (CNN) architecture for long-term activity recognition in egocentric videos. Recognizing long-term activities enables us to temporally segment (index) long and unstructured egocentric videos. Existing methods for this task are based on hand tuned features derived from visible objects, location of hands, as well as optical flow. Given a sparse optical flow volume as input, our CNN classifies the camera wearer's activity. We obtain classification accuracy of 89 , which outperforms the current state-of-the-art by 19 . Additional evaluation is performed on an extended egocentric video dataset, classifying twice the amount of categories than current state-of-the-art. Furthermore, our CNN is able to recognize whether a video is egocentric or not with 99.2 accuracy, up by 24 from current state-of-the-art. To better understand what the network actually learns, we propose a novel visualization of CNN kernels as flow fields. Hands appear very often in egocentric video, and their appearance and pose give important cues about what people are doing and what they are paying attention to. But existing work in hand detection has made strong assumptions that work well in only simple scenarios, such as with limited interaction with other people or in lab settings. We develop methods to locate and distinguish between hands in egocentric video using strong appearance models with Convolutional Neural Networks, and introduce a simple candidate region generation approach that outperforms existing techniques at a fraction of the computational cost. We show how these high-quality bounding boxes can be used to create accurate pixelwise hand regions, and as an application, we investigate the extent to which hand segmentation alone can distinguish between different activities. We evaluate these techniques on a new dataset of 48 first-person videos of people interacting in realistic environments, with pixel-level ground truth for over 15,000 hand instances. This paper introduces the concept of first-person animal activity recognition, the problem of recognizing activities from a view-point of an animal (e.g., a dog). Similar to first-person activity recognition scenarios where humans wear cameras, our approach estimates activities performed by an animal wearing a camera. This enables monitoring and understanding of natural animal behaviors even when there are no people around them. Its applications include automated logging of animal behaviors for medical biology experiments, monitoring of pets, and investigation of wildlife patterns. In this paper, we construct a new dataset composed of first-person animal videos obtained by mounting a camera on each of the four pet dogs. Our new dataset consists of 10 activities containing a heavy fair amount of ego-motion. We implemented multiple baseline approaches to recognize activities from such videos while utilizing multiple types of global local motion features. Animal ego-actions as well as human-animal interactions are recognized with the baseline approaches, and we discuss experimental results. This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multi-channel kernels to integrate global and local motion information, and presents a new activity learning recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.