| 1. |
- Danelljan, Martin, 1989-, et al.
(författare)
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Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
- 2016
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Ingår i: Computer Vision – ECCV 2016. - Cham : Springer. - 9783319464534 - 9783319464541 ; , s. 472-488
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Konferensbidrag (refereegranskat)abstract
- Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.
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| 2. |
- Goutam, Bhat, et al.
(författare)
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Learning What to Learn for Video Object Segmentation
- 2020
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Ingår i: Computer Vision. - Cham : Springer International Publishing. - 9783030585358 - 9783030585365 ; , s. 777-794
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Konferensbidrag (refereegranskat)abstract
- Video object segmentation (VOS) is a highly challengingproblem, since the target object is only defined by a first-frame refer-ence mask during inference. The problem of how to capture and utilizethis limited information to accurately segment the target remains a fun-damental research question. We address this by introducing an end-to-end trainable VOS architecture that integrates a differentiable few-shotlearner. Our learner is designed to predict a powerful parametric modelof the target by minimizing a segmentation error in the first frame. Wefurther go beyond the standard few-shot learning paradigm by learningwhat our target model should learn in order to maximize segmentationaccuracy. We perform extensive experiments on standard benchmarks.Our approach sets a new state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an overall score of 81.5, corresponding toa 2.6% relative improvement over the previous best result. The code andmodels are available at https://github.com/visionml/pytracking.
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| 3. |
- Grelsson, Bertil, 1965-, et al.
(författare)
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HorizonNet for visual terrain navigation
- 2018
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Ingår i: Proceedings of 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728102474 - 9781728102467 - 9781728102481 ; , s. 149-155
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degree panoramic image around the USV. We design a CNN architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model (DEM) data in the Fourier domain using a MOSSE correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in a field trial in the archipelago. Our approach provides promising results by achieving position estimates with GPS-level accuracy.
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| 4. |
- Kristan, M., et al.
(författare)
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The Eighth Visual Object Tracking VOT2020 Challenge Results
- 2020
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Ingår i: Computer Vision. - Cham : Springer International Publishing. - 9783030682378 ; , s. 547-601
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ).
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| 5. |
- Robinson, Andreas, 1975-
(författare)
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Discriminative correlation filters in robot vision
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In less than ten years, deep neural networks have evolved into all-encompassing tools in multiple areas of science and engineering, due to their almost unreasonable effectiveness in modeling complex real-world relationships. In computer vision in particular, they have taken tasks such as object recognition, that were previously considered very difficult, and transformed them into everyday practical tools. However, neural networks have to be trained with supercomputers on massive datasets for hours or days, and this limits their ability adjust to changing conditions.This thesis explores discriminative correlation filters, originally intended for tracking large objects in video, so-called visual object tracking. Unlike neural networks, these filters are small and can be quickly adapted to changes, with minimal data and computing power. At the same time, they can take advantage of the computing infrastructure developed for neural networks and operate within them.The main contributions in this thesis demonstrate the versatility and adaptability of correlation filters for various problems, while complementing the capabilities of deep neural networks. In the first problem, it is shown that when adopted to track small regions and points, they outperform the widely used Lucas-Kanade method, both in terms of robustness and precision. In the second problem, the correlation filters take on a completely new task. Here, they are used to tell different places apart, in a 16 by 16 square kilometer region of ocean near land. Given only a horizon profile - the coast line silhouette of islands and islets as seen from an ocean vessel - it is demonstrated that discriminative correlation filters can effectively distinguish between locations.In the third problem, it is shown how correlation filters can be applied to video object segmentation. This is the task of classifying individual pixels as belonging either to a target or the background, given a segmentation mask provided with the first video frame as the only guidance. It is also shown that discriminative correlation filters and deep neural networks complement each other; where the neural network processes the input video in a content-agnostic way, the filters adapt to specific target objects. The joint function is a real-time video object segmentation method.Finally, the segmentation method is extended beyond binary target/background classification to additionally consider distracting objects. This addresses the fundamental difficulty of coping with objects of similar appearance.
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| 6. |
- Robinson, Andreas, 1975-, et al.
(författare)
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Discriminative Learning and Target Attention for the 2019 DAVIS Challenge onVideo Object Segmentation
- 2019
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Ingår i: CVPR 2019 workshops.
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Konferensbidrag (refereegranskat)abstract
- In this work, we address the problem of semi-supervised video object segmentation, where the task is to segment a target object in every image of the video sequence, given a ground truth only in the first frame. To be successful it is crucial to robustly handle unpredictable target appearance changes and distracting objects in the background. In this work we obtain a robust and efficient representation of the target by integrating a fast and light-weight discriminative target model into a deep segmentation network. Trained during inference, the target model learns to discriminate between the local appearances of target and background image regions. Its predictions are enhanced to accurate segmentation masks in a subsequent refinement stage.To further improve the segmentation performance, we add a new module trained to generate global target attention vectors, given the input mask and image feature maps. The attention vectors add semantic information about thetarget from a previous frame to the refinement stage, complementing the predictions provided by the target appearance model. Our method is fast and requires no network fine-tuning. We achieve a combined J and F-score of 70.6 on the DAVIS 2019 test-challenge data
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| 7. |
- Robinson, Andreas, 1975-, et al.
(författare)
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Distractor-aware video object segmentation
- 2021
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Ingår i: Pattern Recognition. DAGM GCPR 2021. - Cham : Springer International Publishing. - 9783030926588 - 9783030926595 ; , s. 222-234
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Konferensbidrag (refereegranskat)abstract
- Semi-supervised video object segmentation is a challenging task that aims to segment a target throughout a video sequence given an initial mask at the first frame. Discriminative approaches have demonstrated competitive performance on this task at a sensible complexity. These approaches typically formulate the problem as a one-versus-one classification between the target and the background. However, in reality, a video sequence usually encompasses a target, background, and possibly other distracting objects. Those objects increase the risk of introducing false positives, especially if they share visual similarities with the target. Therefore, it is more effective to separate distractors from the background, and handle them independently.We propose a one-versus-many scheme to address this situation by separating distractors into their own class. This separation allows imposing special attention to challenging regions that are most likely to degrade the performance. We demonstrate the prominence of this formulation by modifying the learning-what-to-learn method to be distractor-aware. Our proposed approach sets a new state-of-the-art on the DAVIS val dataset, and improves over the baseline on the DAVIS test-dev benchmark by 4.8 percent points.
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| 8. |
- Robinson, Andreas, 1975-, et al.
(författare)
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Learning Fast and Robust Target Models for Video Object Segmentation
- 2020
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Ingår i: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). - : IEEE. - 9781728171685 ; , s. 7404-7413
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Konferensbidrag (refereegranskat)abstract
- Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. The main difficulty is to effectively handle appearance changes and similar background objects, while maintaining accurate segmentation. Most previous approaches fine-tune segmentation networks on the first frame, resulting in impractical frame-rates and risk of overfitting. More recent methods integrate generative target appearance models, but either achieve limited robustness or require large amounts of training data. We propose a novel VOS architecture consisting of two network components. The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation. The segmentation model is exclusively trained offline, designed to process the coarse scores into high quality segmentation masks. Our method is fast, easily trainable and remains highly effective in cases of limited training data. We perform extensive experiments on the challenging YouTube-VOS and DAVIS datasets. Our network achieves favorable performance, while operating at higher frame-rates compared to state-of-the-art. Code and trained models are available at https://github.com/andr345/frtm-vos.
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| 9. |
- Robinson, Andreas, 1975-, et al.
(författare)
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Robust Accurate Extrinsic Calibration of Static Non-overlapping Cameras
- 2017
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Ingår i: Computer Analysis of Images and Patterns. - Cham : Springer. - 9783319646978 - 9783319646985 ; , s. 342-353
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Konferensbidrag (refereegranskat)abstract
- An increasing number of robots and autonomous vehicles are equipped with multiple cameras to achieve surround-view sensing. The estimation of their relative poses, also known as extrinsic parameter calibration, is a challenging problem, particularly in the non-overlapping case. We present a simple and novel extrinsic calibration method based on standard components that performs favorably to existing approaches. We further propose a framework for predicting the performance of different calibration configurations and intuitive error metrics. This makes selecting a good camera configuration straightforward. We evaluate on rendered synthetic images and show good results as measured by angular and absolute pose differences, as well as the reprojection error distributions.
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| 10. |
- Zhang, Yushan, Doktorand, et al.
(författare)
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Leveraging Optical Flow Features for Higher Generalization Power in Video Object Segmentation
- 2023
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Ingår i: 2023 IEEEInternational Conferenceon Image Processing. - : IEEE. - 9781728198354 - 9781728198361 ; , s. 326-330
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Konferensbidrag (refereegranskat)abstract
- We propose to leverage optical flow features for higher generalization power in semi-supervised video object segmentation. Optical flow is usually exploited as additional guidance information in many computer vision tasks. However, its relevance in video object segmentation was mainly in unsupervised settings or using the optical flow to warp or refine the previously predicted masks. Different from the latter, we propose to directly leverage the optical flow features in the target representation. We show that this enriched representation improves the encoder-decoder approach to the segmentation task. A model to extract the combined information from the optical flow and the image is proposed, which is then used as input to the target model and the decoder network. Unlike previous methods, e.g. in tracking where concatenation is used to integrate information from image data and optical flow, a simple yet effective attention mechanism is exploited in our work. Experiments on DAVIS 2017 and YouTube-VOS 2019 show that integrating the information extracted from optical flow into the original image branch results in a strong performance gain, especially in unseen classes which demonstrates its higher generalization power.
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