1,720,973 research outputs found
Wearable Vision for Retrieving Architectural Details in Augmented Tourist Experiences
No full textThe interest in cultural cities is in constant growth, and so is the demand for new multimedia tools and applications that enrich their fruition. In this paper we propose an egocentric vision system to enhance tourists' cultural heritage experience.
Exploiting a wearable board and a glass-mounted camera, the visitor can retrieve architectural details of the historical building he is observing and receive related multimedia contents. To obtain an effective retrieval procedure we propose a visual descriptor based on the covariance of local features. Differently than the common Bag of Words approaches our feature vector does not rely on a generated visual vocabulary, removing the dependence from a specific dataset and obtaining a reduction of the computational cost. 3D modeling is used to achieve a precise visitor's localization that allows browsing visible relevant details that the user may otherwise miss.
Experimental results conducted on a publicly available cultural heritage dataset show that the proposed feature descriptor outperforms Bag of Words techniques
Wearable Vision for Retrieving Architectural Details in Augmented Tourist Experiences
No full textThe interest in cultural cities is in constant growth, and so is the demand for new multimedia tools and applications that enrich their fruition. In this paper we propose an egocentric vision system to enhance tourists' cultural heritage experience. Exploiting a wearable board and a glass-mounted camera, the visitor can retrieve architectural details of the historical building he is observing and receive related multimedia contents. To obtain an effective retrieval procedure we propose a visual descriptor based on the covariance of local features. Differently than the common Bag of Words approaches our feature vector does not rely on a generated visual vocabulary, removing the dependence from a specific dataset and obtaining a reduction of the computational cost. 3D modeling is used to achieve a precise visitor's localization that allows browsing visible relevant details that the user may otherwise miss. Experimental results conducted on a publicly available cultural heritage dataset show that the proposed feature descriptor outperforms Bag of Words techniques
Video registration in egocentric vision under day and night illumination changes
No full textWith the spread of wearable devices and head mounted cameras, a wide range of application requiring precise user localization is now possible. In this paper we propose to treat the problem of obtaining the user position with respect to a known environment as a video registration problem. Video registration, i.e. the task of aligning an input video sequence to a pre-built 3D model, relies on a matching process of local keypoints extracted on the query sequence to a 3D point cloud. The overall registration performance is strictly tied to the actual quality of this 2D-3D matching, and can degrade if environmental conditions such as steep changes in lighting like the ones between day and night occur. To effectively register an egocentric video sequence under these conditions, we propose to tackle the source of the problem: the matching process. To overcome the shortcomings of standard matching techniques, we introduce a novel embedding space that allows us to obtain robust matches by jointly taking into account local descriptors, their spatial arrangement and their temporal robustness. The proposal is evaluated using unconstrained egocentric video sequences both in terms of matching quality and resulting registration performance using different 3D models of historical landmarks. The results show that the proposed method can outperform state of the art registration algorithms, in particular when dealing with the challenges of night and day sequences
Can adversarial networks hallucinate occluded people with a plausible aspect?
Full text linkWhen you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world
Self-Supervised Optical Flow Estimation by Projective Bootstrap
No full textDense optical flow estimation is complex and time consuming, with state-of-the-art methods relying either on large synthetic data sets or on pipelines requiring up to a few minutes per frame pair. In this paper, we address the problem of optical flow estimation in the automotive scenario in a self-supervised manner. We argue that optical flow can be cast as a geometrical warping between two successive video frames and devise a deep architecture to estimate such transformation in two stages. First, a dense pixel-level flow is computed with a projective bootstrap on rigid surfaces. We show how such global transformation can be approximated with a homography and extend spatial transformer layers so that they can be employed to compute the flow field implied by such transformation. Subsequently, we refine the prediction by feeding a second, deeper network that accounts for moving objects. A final reconstruction loss compares the warping of frame Xt with the subsequent frame Xt+1 and guides both estimates. The model has the speed advantages of end-to-end deep architectures while achieving competitive performances, both outperforming recent unsupervised methods and showing good generalization capabilities on new automotive data sets
Understanding social relationships in egocentric vision
Full text linkThe understanding of mutual people interaction is a key component for recognizing people social behavior, but it strongly relies on a personal point of view resulting difficult to be a-priori modeled. We propose the adoption of the unique head mounted cameras first person perspective (ego-vision) to promptly detect people interaction in different social contexts. The proposal relies on a complete and reliable system that extracts people׳s head pose combining landmarks and shape descriptors in a temporal smoothed HMM framework. Finally, interactions are detected through supervised clustering on mutual head orientation and people distances exploiting a structural learning framework that specifically adjusts the clustering measure according to a peculiar scenario. Our solution provides the flexibility to capture the interactions disregarding the number of individuals involved and their level of acquaintance in context with a variable degree of social involvement. The proposed system shows competitive performances on both publicly available ego-vision datasets and ad hoc benchmarks built with real life situations
Motion Segmentation using Visual and Bio-mechanical Features
No full textNowadays, egocentric wearable devices are continuously increasing their widespread among both the academic community and the general public. For this reason, methods capable of automatically segment the video based on the recorder motion patterns are gaining attention. These devices present the unique opportunity of both high quality video recordings and multimodal sensors readings. Significant efforts have been made in either analyzing the video stream recorded by these devices or the bio-mechanical sensor information. So far, the integration between these two realities has not been fully addressed, and the real capabilities of these devices are not yet exploited.
In this paper, we present a solution to segment a video sequence into motion activities by introducing a novel data fusion technique based on the covariance of visual and bio-mechanical features.
The experimental results are promising and show that the proposed integration strategy outperforms the results achieved focusing solely on a single source
Head Pose Estimation in First-Person Camera Views
No full textIn this paper we present a new method for head pose real-time estimation in ego-vision scenarios that is a key step in the understanding of social interactions. In order to robustly detect head under changing aspect ratio, scale and orientation we use and extend the Hough-Based Tracker which allows to follow simultaneously each subject in the scene. In an ego-vision scenario where a group interacts in a discussion, each subject's head orientation will be more likely to remain focused for a while on the person who has the floor. In order to encode this behavior we include a stateful Hidden Markov Model technique that enforces the predicted pose with the temporal coherence from a video sequence. We extensively test our approach on several indoor and outdoor ego-vision videos with high illumination variations showing its validity and outperforming other recent related state of the art approaches
Exploring Architectural Details Through aWearable Egocentric Vision Device
Full text linkAugmented user experiences in the cultural heritage domain are in increasing
demand by the new digital native tourists of 21st century. In this paper, we propose a novel
solution that aims at assisting the visitor during an outdoor tour of a cultural site using the
unique first person perspective of wearable cameras. In particular, the approach exploits
computer vision techniques to retrieve the details by proposing a robust descriptor based on
the covariance of local features. Using a lightweight wearable board the solution can localize
the user with respect to the 3D point cloud of the historical landmark and provide him with
information about the details he is currently looking at. Experimental results validate the
method both in terms of accuracy and computational effort. Furthermore, user evaluation
based on real-world experiments shows that the proposal is deemed effective in enriching a
cultural experience
DR(eye)VE: a Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving
No full textAutonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations
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