Karel Lebeda
University of Surrey
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Publication
Featured researches published by Karel Lebeda.
european conference on computer vision | 2016
Matej Kristan; Roman P. Pflugfelder; Aleš Leonardis; Jiri Matas; Luka Cehovin; Georg Nebehay; Tomas Vojir; Gustavo Fernández; Alan Lukezic; Aleksandar Dimitriev; Alfredo Petrosino; Amir Saffari; Bo Li; Bohyung Han; CherKeng Heng; Christophe Garcia; Dominik Pangersic; Gustav Häger; Fahad Shahbaz Khan; Franci Oven; Horst Bischof; Hyeonseob Nam; Jianke Zhu; Jijia Li; Jin Young Choi; Jin-Woo Choi; João F. Henriques; Joost van de Weijer; Jorge Batista; Karel Lebeda
Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).
british machine vision conference | 2012
Karel Lebeda; Jiri Matas; Ondrej Chum
The paper revisits the problem of local optimization for RANSAC. Improvements of the LO-RANSAC procedure are proposed: a use of truncated quadratic cost function, an introduction of a limit on the number of inliers used for the least squares computation and several implementation issues are addressed. The implementation is made publicly available. Extensive experiments demonstrate that the novel algorithm called LO + -RANSAC is (1) very stable (almost non-random in nature), (2) very precise in a broad range of con- ditions, (3) less sensitive to the choice of inlier-outlier threshold and (4) it offers a sig- nificantly better starting point for bundle adjustment than the Gold Standard method advocated in the Hartley-Zisserman book.
international conference on computer vision | 2013
Karel Lebeda; Simon Hadfield; Jiri Matas; Richard Bowden
Long term tracking of an object, given only a single instance in an initial frame, remains an open problem. We propose a visual tracking algorithm, robust to many of the difficulties which often occur in real-world scenes. Correspondences of edge-based features are used, to overcome the reliance on the texture of the tracked object and improve invariance to lighting. Furthermore we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or occlusion. The two-module principle is similar to the successful state-of-the-art long-term TLD tracker, however our approach extends to cases of low-textured objects. Besides reporting our results on the VOT Challenge dataset, we perform two additional experiments. Firstly, results on short-term sequences show the performance of tracking challenging objects which represent failure cases for competing state-of-the-art approaches. Secondly, long sequences are tracked, including one of almost 30000 frames which to our knowledge is the longest tracking sequence reported to date. This tests the re-detection and drift resistance properties of the tracker. All the results are comparable to the state-of-the-art on sequences with textured objects and superior on non-textured objects. The new annotated sequences are made publicly available.
european conference on computer vision | 2014
Simon Hadfield; Karel Lebeda; Richard Bowden
We investigate the recognition of actions “in the wild” using 3D motion information. The lack of control over (and knowledge of) the camera configuration, exacerbates this already challenging task, by introducing systematic projective inconsistencies between 3D motion fields, hugely increasing intra-class variance. By introducing a robust, sequence based, stereo calibration technique, we reduce these inconsistencies from fully projective to a simple similarity transform. We then introduce motion encoding techniques which provide the necessary scale invariance, along with additional invariances to changes in camera viewpoint.
asian conference on computer vision | 2014
Karel Lebeda; Simon Hadfield; Richard Bowden
In this paper, we address the problem of tracking an unknown object in 3D space. Online 2D tracking often fails for strong out-of-plane rotation which results in considerable changes in appearance beyond those that can be represented by online update strategies. However, by modelling and learning the 3D structure of the object explicitly, such effects are mitigated. To address this, a novel approach is presented, combining techniques from the fields of visual tracking, structure from motion (SfM) and simultaneous localisation and mapping (SLAM). This algorithm is referred to as TMAGIC (Tracking, Modelling And Gaussian-process Inference Combined). At every frame, point and line features are tracked in the image plane and are used, together with their 3D correspondences, to estimate the camera pose. These features are also used to model the 3D shape of the object as a Gaussian process. Tracking determines the trajectories of the object in both the image plane and 3D space, but the approach also provides the 3D object shape. The approach is validated on several video-sequences used in the tracking literature, comparing favourably to state-of-the-art trackers for simple scenes (error reduced by 22 %) with clear advantages in the case of strong out-of-plane rotation, where 2D approaches fail (error reduction of 58 %).
IEEE Transactions on Image Processing | 2016
Karel Lebeda; Simon Hadfield; Jiri Matas; Richard Bowden
Long-term tracking of an object, given only a single instance in an initial frame, remains an open problem. We propose a visual tracking algorithm, robust to many of the difficulties that often occur in real-world scenes. Correspondences of edge-based features are used, to overcome the reliance on the texture of the tracked object and improve invariance to lighting. Furthermore, we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or occlusion. The two-module principle is similar to the successful state-of-the-art long-term TLD tracker; however, our approach offers better performance in benchmarks and extends to cases of low-textured objects. This becomes obvious in cases of plain objects with no texture at all, where the edge-based approach proves the most beneficial. We perform several different experiments to validate the proposed method. First, results on short-term sequences show the performance of tracking challenging (low textured and/or transparent) objects that represent failure cases for competing the state-of-the-art approaches. Second, long sequences are tracked, including one of almost 30 000 frames, which, to the best of our knowledge, is the longest tracking sequence reported to date. This tests the redetection and drift resistance properties of the tracker. Finally, we report the results of the proposed tracker on the VOT Challenge 2013 and 2014 data sets as well as on the VTB1.0 benchmark, and we show relative performance of the tracker compared with its competitors. All the results are comparable with the state of the art on sequences with textured objects and superior on non-textured objects. The new annotated sequences are made publicly available.
international conference on computer vision | 2012
Karel Lebeda; Jiri Matas; Richard Bowden
We propose a novel approach to tracking objects by low-level line correspondences. In our implementation we show that this approach is usable even when tracking objects with lack of texture, exploiting situations, when feature-based trackers fails due to the aperture problem. Furthermore, we suggest an approach to failure detection and recovery to maintain long-term stability. This is achieved by remembering configurations which lead to good pose estimations and using them later for tracking corrections. We carried out experiments on several sequences of different types. The proposed tracker proves itself as competitive or superior to state-of-the-art trackers in both standard and low-textured scenes.
International Journal of Computer Vision | 2017
Simon Hadfield; Karel Lebeda; Richard Bowden
Action recognition “in the wild” is extremely challenging, particularly when complex 3D actions are projected down to the image plane, losing a great deal of information. The recent growth of 3D data in broadcast content and commercial depth sensors, makes it possible to overcome this. However, there is little work examining the best way to exploit this new modality. In this paper we introduce the Hollywood 3D benchmark, which is the first dataset containing “in the wild” action footage including 3D data. This dataset consists of 650 stereo video clips across 14 action classes, taken from Hollywood movies. We provide stereo calibrations and depth reconstructions for each clip. We also provide an action recognition pipeline, and propose a number of specialised depth-aware techniques including five interest point detectors and three feature descriptors. Extensive tests allow evaluation of different appearance and depth encoding schemes. Our novel techniques exploiting this depth allow us to reach performance levels more than triple those of the best baseline algorithm using only appearance information. The benchmark data, code and calibrations are all made available to the community.
european conference on computer vision | 2016
Michael Felsberg; Matej Kristan; Aleš Leonardis; Roman P. Pflugfelder; Gustav Häger; Amanda Berg; Abdelrahman Eldesokey; Jörgen Ahlberg; Luka Cehovin; Tomáš Vojír̃; Alan Lukežič; Gustavo Fernández; Alfredo Petrosino; Álvaro García-Martín; Andres Solis Montero; Anton Varfolomieiev; Aykut Erdem; Bohyung Han; Chang-Ming Chang; Dawei Du; Erkut Erdem; Fahad Shahbaz Khan; Fatih Porikli; Fei Zhao; Filiz Bunyak; Francesco Battistone; Gao Zhu; Hongdong Li; Honggang Qi; Horst Bischof
The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link -- ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.
international conference on computer vision | 2015
Karel Lebeda; Simon Hadfield; Richard Bowden
Causal relationships can often be found in visual object tracking between the motions of the camera and that of the tracked object. This object motion may be an effect of the camera motion, e.g. an unsteady handheld camera. But it may also be the cause, e.g. the cameraman framing the object. In this paper we explore these relationships, and provide statistical tools to detect and quantify them, these are based on transfer entropy and stem from information theory. The relationships are then exploited to make predictions about the object location. The approach is shown to be an excellent measure for describing such relationships. On the VOT2013 dataset the prediction accuracy is increased by 62 % over the best non-causal predictor. We show that the location predictions are robust to camera shake and sudden motion, which is invaluable for any tracking algorithm and demonstrate this by applying causal prediction to two state-of-the-art trackers. Both of them benefit, Struck gaining a 7 % accuracy and 22 % robustness increase on the VTB1.1 benchmark, becoming the new state-of-the-art.