Indoor localisation based on fusing WLAN and image data

In this paper we address the automatic identification of indoor locations using a combination of WLAN and image sensing. We demonstrate the effectiveness of combining the strengths of these two complementary modalities for very chal- lenging data. We describe a fusion approach that allows localising to a specific office within a building to a high degree of precision or to a location within that office with reasonable precision. As it can be orientated towards the needs and capabilities of a user based on context the method becomes useful for ambient assisted living applications

Dual-sensor fusion for indoor user localisation

In this paper we address the automatic identification of in- door locations using a combination of WLAN and image sensing. Our motivation is the increasing prevalence of wear- able cameras, some of which can also capture WLAN data. We propose to use image-based and WLAN-based localisa- tion individually and then fuse the results to obtain better performance overall. We demonstrate the effectiveness of our fusion algorithm for localisation to within a 8.9m2 room on very challenging data both for WLAN and image-based algorithms. We envisage the potential usefulness of our ap- proach in a range of ambient assisted living applications

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Semantic Visual Localization

Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed. Our method leverages a novel generative model for descriptor learning, trained on semantic scene completion as an auxiliary task. The resulting 3D descriptors are robust to missing observations by encoding high-level 3D geometric and semantic information. Experiments on several challenging large-scale localization datasets demonstrate reliable localization under extreme viewpoint, illumination, and geometry changes

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Efficient 2D-3D Matching for Multi-Camera Visual Localization

Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multicamera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This significantly accelerates the matching and pose estimation stages and allows us to dynamically adapt the matching efforts based on the surrounding environment. In addition, we show how pose priors can be integrated into the localization system to increase efficiency and robustness. Finally, we extend our algorithm by fusing the absolute pose estimates with motion estimates from a multi-camera visual inertial odometry pipeline (VIO). This results in a system that provides reliable and drift-less pose estimation. Extensive experiments show that our localization runs fast and robust under varying conditions, and that our extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure