| Home | [171–180] << 181 182 183 184 185 186 187 188 189 190 >> [191–200] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
| Title | Seamless Human Motion Composition with Blended Positional Encodings | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
Keywords  |
|||||
| Abstract | Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BEP2024 | Serial | 4022 | ||
| Permanent link to this record | |||||
| Author | Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal | ||||
| Title | GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords |
|||||
| Abstract | Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BBL2024b | Serial | 4023 | ||
| Permanent link to this record | |||||
| Author | Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang | ||||
| Title | Diffusion-based network for unsupervised landmark detection | Type | Journal Article | ||
| Year | 2024 | Publication | Knowledge-Based Systems | Abbreviated Journal | |
| Volume | 292 | Issue | Pages | 111627 | |
| Keywords |
|||||
| Abstract | Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WWT2024 | Serial | 4024 | ||
| Permanent link to this record | |||||
| Author | Nicola Bellotto; Eric Sommerlade; Ben Benfold; Charles Bibby; I. Reid; Daniel Roth; Luc Van Gool; Carles Fernandez; Jordi Gonzalez | ||||
| Title | A Distributed Camera System for Multi-Resolution Surveillance | Type | Conference Article | ||
| Year | 2009 | Publication | 3rd ACM/IEEE International Conference on Distributed Smart Cameras | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords |
10.1109/ICDSC.2009.5289413 | ||||
| Abstract | We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance. | ||||
| Address | Como, Italy | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDSC | ||
| Notes | Approved | no | |||
| Call Number | ISE @ ise @ BSB2009 | Serial | 1205 | ||
| Permanent link to this record | |||||
| Author | F.Guirado; Ana Ripoll; C.Roig; Aura Hernandez-Sabate; Emilio Luque | ||||
| Title | Exploiting Throughput for Pipeline Execution in Streaming Image Processing Applications | Type | Book Chapter | ||
| Year | 2006 | Publication | Euro-Par 2006 Parallel Processing | Abbreviated Journal | LNCS |
| Volume | 4128 | Issue | Pages | 1095-1105 | |
| Keywords |
12th International Euro–Par Conference | ||||
| Abstract | There is a large range of image processing applications that act on an input sequence of image frames that are continuously received. Throughput is a key performance measure to be optimized when execu- ting them. In this paper we propose a new task replication methodology for optimizing throughput for an image processing application in the field of medicine. The results show that by applying the proposed methodo- logy we are able to achieve the desired throughput in all cases, in such a way that the input frames can be processed at any given rate. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Dresden, Germany (European Union) | Editor | UAB; W, E.N.; et al. |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Lecture Notes In Computer Science | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | Euro–Par | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GRR2006a | Serial | 1542 | ||
| Permanent link to this record | |||||
| Author | Fadi Dornaika; Bogdan Raducanu | ||||
| Title | Single Snapshot 3D Head Pose Initialization for Tracking in Human Robot Interaction Scenario | Type | Conference Article | ||
| Year | 2010 | Publication | 1st International Workshop on Computer Vision for Human-Robot Interaction | Abbreviated Journal | |
| Volume | Issue | Pages | 32–39 | ||
| Keywords |
1st International Workshop on Computer Vision for Human-Robot Interaction, in conjunction with IEEE CVPR 2010 | ||||
| Abstract | This paper presents an automatic 3D head pose initialization scheme for a real-time face tracker with application to human-robot interaction. It has two main contributions. First, we propose an automatic 3D head pose and person specific face shape estimation, based on a 3D deformable model. The proposed approach serves to initialize our realtime 3D face tracker. What makes this contribution very attractive is that the initialization step can cope with faces
under arbitrary pose, so it is not limited only to near-frontal views. Second, the previous framework is used to develop an application in which the orientation of an AIBO’s camera can be controlled through the imitation of user’s head pose. In our scenario, this application is used to build panoramic images from overlapping snapshots. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
||||
| Address | San Francisco; CA; USA; June 2010 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2160-7508 | ISBN | 978-1-4244-7029-7 | Medium | |
| Area | Expedition | Conference | CVPRW | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ DoR2010a | Serial | 1309 | ||
| Permanent link to this record | |||||
| Author | Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti | ||||
| Title | Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control | Abbreviated Journal | T-UFFC |
| Volume | 58 | Issue | 1 | Pages | 60-72 |
| Keywords |
3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging | ||||
| Abstract | Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0885-3010 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM;ADAS | Approved | no | ||
| Call Number | IAM @ iam @ HGG2011 | Serial | 1546 | ||
| Permanent link to this record | |||||
| Author | Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras | ||||
| Title | Multi-part body segmentation based on depth maps for soft biometry analysis | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 56 | Issue | Pages | 14-21 | |
| Keywords |
3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis | ||||
| Abstract | This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB | Approved | no | ||
| Call Number | Admin @ si @ MEG2015 | Serial | 2588 | ||
| Permanent link to this record | |||||
| Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
| Title | Single view facial hair 3D reconstruction | Type | Conference Article | ||
| Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 11867 | Issue | Pages | 423-436 | |
| Keywords |
3D Vision; Shape Reconstruction; Facial Hair Modeling | ||||
| Abstract | n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. | ||||
| Address | Madrid; July 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3707 | ||
| Permanent link to this record | |||||
| Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
| Title | Detailed 3D face reconstruction from a single RGB image | Type | Journal | ||
| Year | 2019 | Publication | Journal of WSCG | Abbreviated Journal | JWSCG |
| Volume | 27 | Issue | 2 | Pages | 103-112 |
| Keywords |
3D Wrinkle Reconstruction; Face Analysis, Optimization. | ||||
| Abstract | This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
||||
| Address | 2019/11 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3708 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Jialuo Chen; Alicia Fornes; Beata Megyesi | ||||
| Title | Towards a generic unsupervised method for transcription of encoded manuscripts | Type | Conference Article | ||
| Year | 2019 | Publication | 3rd International Conference on Digital Access to Textual Cultural Heritage | Abbreviated Journal | |
| Volume | Issue | Pages | 73-78 | ||
| Keywords |
A. Baró, J. Chen, A. Fornés, B. Megyesi. | ||||
| Abstract | Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods. | ||||
| Address | Brussels; May 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DATeCH | ||
| Notes | DAG; 600.097; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BCF2019 | Serial | 3276 | ||
| Permanent link to this record | |||||
| Author | T.Chauhan; E.Perales; Kaida Xiao; E.Hird ; Dimosthenis Karatzas; Sophie Wuerger | ||||
| Title | The achromatic locus: Effect of navigation direction in color space | Type | Journal Article | ||
| Year | 2014 | Publication | Journal of Vision | Abbreviated Journal | VSS |
| Volume | 14 (1) | Issue | 25 | Pages | 1-11 |
| Keywords |
achromatic; unique hues; color constancy; luminance; color space | ||||
| Abstract | 5Y Impact Factor: 2.99 / 1st (Ophthalmology)
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m2). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CPX2014 | Serial | 2418 | ||
| Permanent link to this record | |||||
| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma | ||||
| Title | Scale coding bag of deep features for human attribute and action recognition | Type | Journal Article | ||
| Year | 2018 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | 29 | Issue | 1 | Pages | 55-71 |
| Keywords |
Action recognition; Attribute recognition; Bag of deep features | ||||
| Abstract | Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.068; 600.079; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ KWR2018 | Serial | 3107 | ||
| Permanent link to this record | |||||
| Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen | ||||
| Title | Top-Down Deep Appearance Attention for Action Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 20th Scandinavian Conference on Image Analysis | Abbreviated Journal | |
| Volume | 10269 | Issue | Pages | 297-309 | |
| Keywords |
Action recognition; CNNs; Feature fusion | ||||
| Abstract | Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches. | ||||
| Address | Tromso; June 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | SCIA | ||
| Notes | LAMP; 600.109; 600.068; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ RKW2017b | Serial | 3039 | ||
| Permanent link to this record | |||||
| Author | Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro | ||||
| Title | Residual Stacked RNNs for Action Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 9th International Workshop on Human Behavior Understanding | Abbreviated Journal | |
| Volume | Issue | Pages | 534-548 | ||
| Keywords |
Action recognition; Deep residual learning; Two-stream RNN | ||||
| Abstract | Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. | ||||
| Address | Munich; September 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LCE2018b | Serial | 3206 | ||
| Permanent link to this record | |||||