Showing papers by "Jiri Matas published in 2014"

The Visual Object Tracking VOT2014 challenge results

Abstract: The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

Detection, Rectification and Segmentation of Coplanar Repeated Patterns

Abstract: This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns. The only assumption made of the scene geometry is that repeated scene elements are mapped to each other by planar Euclidean transformations. The class of patterns covered is broad and includes nearly all commonly seen, planar, man-made repeated patterns. In addition, novel linear constraints are used to reduce geometric ambiguity between the rectified imaged pattern and the scene pattern. Rectification to within a similarity of the scene plane is achieved from one rotated repeat, or to within a similarity with a scale ambiguity along the axis of symmetry from one reflected repeat. A stratum of constraints is derived that gives the necessary configuration of repeats for each successive level of rectification. A generative model for the imaged pattern is inferred and used to segment the pattern with pixel accuracy. Qualitative results are shown on a broad range of image types on which state-of-the-art methods fail.

Texture-Based Leaf Identification

Abstract: A novel approach to visual leaf identification is proposed. A leaf is represented by a pair of local feature histograms, one computed from the leaf interior, the other from the border. The histogrammed local features are an improved version of a recently proposed rotation and scale invariant descriptor based on local binary patterns (LBPs).

The VOT2013 challenge: overview and additional results

Abstract: 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) challenge and 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). In this paper we provide an overview of the VOT2013 challenge, point out its main results and document the additional previously unpublished experiments and results.

Fast Features Invariant to Rotation and Scale of Texture

Abstract: A family of novel texture representations called Ffirst, the Fast Features Invariant to Rotation and Scale of Texture, is introduced. New rotation invariants are proposed, extending the LBP-HF features, improving the recognition accuracy. Using the full set of LBP features, as opposed to uniform only, leads to further improvement. Linear Support Vector Machines with an approximate \(\chi ^2\)-kernel map are used for fast and precise classification.

A 3D Approach to Facial Landmarks: Detection, Refinement, and Tracking

Abstract: A real-time algorithm for accurate localization of facial landmarks in a single monocular image is proposed. The algorithm is formulated as an optimization problem, in which the sum of responses of local classifiers is maximized with respect to the camera pose by fitting a generic (not a person-specific) 3D model. The algorithm simultaneously estimates a head position and orientation and detects the facial landmarks in the image. Despite being local, we show that the basin of attraction is large to the extent it can be initialized by a scanning window face detector. Other experiments on standard datasets demonstrate that the proposed algorithm outperforms a state-of-the-art landmark detector especially for non-frontal face images, and that it is capable of reliable and stable tracking for large set of viewing angles.

Rectification, and Segmentation of Coplanar Repeated Patterns

Abstract: This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns The only assumption made of the scene geometry is that repeated scene elements are mapped to each other by planar Euclidean transformations The class of patterns covered is broad and includes nearly all commonly seen, planar, man-made repeated patterns In addition, novel linear constraints are used to reduce geometric ambiguity between the rectified imaged pattern and the scene pattern Rectification to within a similarity of the scene plane is achieved from one rotated repeat, or to within a similarity with a scale ambiguity along the axis of symmetry from one reflected repeat A stratum of constraints is derived that gives the necessary configuration of repeats for each successive level of rectification A generative model for the imaged pattern is inferred and used to segment the pattern with pixel accuracy Qualitative results are shown on a broad range of image types on which state-of-the-art methods fail