Going Deeper into First-Person Activity Recognition

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 integrate these ideas under one framework, we propose a twin stream network architecture, where one stream analyzes appearance information and the other stream analyzes motion information. Our appearance stream encodes prior knowledge of the egocentric paradigm by explicitly training the network to segment hands and localize objects. By visualizing certain neuron activation of our network, we show that our proposed architecture naturally learns features that capture object attributes and hand-object configurations. Our extensive experiments on benchmark egocentric action datasets show that our deep architecture enables recognition rates that significantly outperform state-of-the-art techniques -- an average $6.6\%$ increase in accuracy over all datasets. Furthermore, by learning to recognize objects, actions and activities jointly, the performance of individual recognition tasks also increase by $30\%$ (actions) and $14\%$ (objects). We also include the results of extensive ablative analysis to highlight the importance of network design decisions.

Boosted Multiple Kernel Learning for First-Person Activity Recognition

Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras. There has been a surge of efforts adapting existing feature descriptors and designing new descriptors for the first-person videos. An effective activity recognition system requires selection and use of complementary features and appropriate kernels for each feature. In this study, we propose a data-driven framework for first-person activity recognition which effectively selects and combines features and their respective kernels during the training. Our experimental results show that use of Multiple Kernel Learning (MKL) and Boosted MKL in first-person activity recognition problem exhibits improved results in comparison to the state-of-the-art. In addition, these techniques enable the expansion of the framework with new features in an efficient and convenient way.Comment: First published in the Proceedings of the 25th European Signal Processing Conference (EUSIPCO-2017) in 2017, published by EURASI

Semi-Supervised First-Person Activity Recognition in Body-Worn Video

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video

First-person activity recognition: how to generalize to unseen users?

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)Recent advances in wearable technology, accompanied by the decreasing cost of data storage and increase of data availability have made possible to take pictures everywhere at every time. Wearable cameras are nowadays among the most popular wearable devices. Besides leisure, wearable cameras are attracting a lot of attention for the improvement of working conditions, productivity and safety monitoring. Since the collected data can be potentially used for memory training and extracting lifestyle patterns useful to prevent noncommunicable diseases as obesity, they are being investigated in the context of Preventive Medicine. Most of these applications require to automatically recognize the ability performed by the user. This work aims to make a step forwards towards activity recognition from photo-streams captured by a wearable camera by developing a method that allows to label new images with minial effort from the user and generalize well for unseen users

First-Person Activity Recognition: What Are They Doing to Me?

This paper discusses the problem of recognizing interaction-level human activities from a first-person view-point. 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 in-formation, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experi-ments, we not only show classification results with seg-mented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably. 1

Leveraging over depth in egocentric activity recognition

Activity recognition from first person videos is a growing research area. The increasing diffusion of egocentric sensors in various devices makes it timely to develop approaches able to recognize fine grained first person actions like picking up, putting down, pouring and so forth. While most of previous work focused on RGB data, some authors pointed out the importance of leveraging over depth information in this domain. In this paper we follow this trend and we propose the first deep architecture that uses depth maps as an attention mechanism for first person activity recognition. Specifically, we blend together the RGB and depth data, so to obtain an enriched input for the network. This blending puts more or less emphasis on different parts of the image based on their distance from the observer, hence acting as an attention mechanism. To further strengthen the proposed activity recognition protocol, we opt for a self labeling approach. This, combined with a Conv-LSTM block for extracting temporal information from the various frames, leads to the new state of the art on two publicly available benchmark databases. An ablation study completes our experimental findings, confirming the effectiveness of our approac

Early Recognition of Human Activities from First-Person Videos Using Onset Representations

In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos