Vincent Spruyt

Real-time multi-colourspace hand segmentation

This paper proposes an accurate real-time hand tracking and segmentation algorithm. A particle filter tracks the hands in time, based on colour and motion cues. This filter is able to automatically recover from failures and does not need an initialization phase. The algorithm is proven to be robust against lighting changes, and can be used in unconstrained environments. Hand segmentation is based on a Gaussian Mixture Model and refined using a combination of spatial information. Cues from both HSV and RGB colour space are used to increase robustness.

HEp-2 cell pattern segmentation for the support of autoimmune disease diagnosis

The Indirect Immune Fluorescence Test (iIFT) is the most commonly used screening method for the diagnosis of autoimmune diseases. The presence of certain autoimmune diseases is proven by immunologically detecting their corresponding auto-antibodies using the HEp-2 cancer cell line. For this purpose HEp-2 cells are added to the patients blood serum containing certain auto-antibodies which will bond with the HEp-2 cells leading to a wide variety of patterns that can be observed under a fluorescence microscope. Due to the disadvantages of manual testing, automation and standardization are necessary. This paper proposes an unsupervised segmentation algorithm as part of an ongoing research to develop a CAD system to digitally support iIFT testing.

Real-time hand tracking by invariant hough forest detection

This paper proposes a robust real-time hand tracking approach by combining a discriminative random forest classifier with generative color based cues using a particle filter. The proposed detector is scale and rotation invariant and is able to overcome ambiguities and local maxima in the color based likelihood function in real-time. A new hand tracking dataset with manually annotated groundtruths is created and made freely available for research purposes. Thorough evaluation shows the robustness and advantages of our proposal compared to other state of the art object tracking methods.

Real-time, long-term hand tracking with unsupervised initialization

This paper proposes a complete tracking system that is capable of long-term, real-time hand tracking with unsupervised initialization and error recovery. Initialization is steered by a three-stage hand detector, combining spatial and temporal information. Hand hypotheses are generated by a random forest detector in the first stage, whereas a simple linear classifier eliminates false positive detections. Resulting detections are tracked by particle filters that gather temporal statistics in order to make a final decision. The detector is scale and rotation invariant, and can detect hands in any pose in unconstrained environments. The resulting discriminative confidence map is combined with a generative particle filter based observation model to enable robust, long-term hand tracking in real-time. The proposed solution is evaluated using several challenging, publicly available datasets, and is shown to clearly outperform other state of the art object tracking methods.

Robust arm and hand tracking by unsupervised context learning.

Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character of the human hand. In this paper, we propose an unsupervised method to automatically learn the context in which a hand is embedded. This context includes the arm and any other object that coherently moves along with the hand. We introduce two novel methods to incorporate this context information into a probabilistic tracking framework, and introduce a simple yet effective solution to estimate the position of the arm. Finally, we show that our method greatly increases robustness against occlusion and cluttered background, without degrading tracking performance if no contextual information is available. The proposed real-time algorithm is shown to outperform the current state-of-the-art by evaluating it on three publicly available video datasets. Furthermore, a novel dataset is created and made publicly available for the research community.

Sparse optical flow regularization for real-time visual tracking

Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes.

A Canonical Correlation Analysis based motion model for probabilistic visual tracking

Particle filters are often used for tracking objects within a scene. As the prediction model of a particle filter is often implemented using basic movement predictions such as random walk, constant velocity or acceleration, these models will usually be incorrect. Therefore, this paper proposes a new approach, based on a Canonical Correlation Analysis (CCA) tracking method which provides an object specific motion model. This model is used to construct a proposal distribution of the prediction model which predicts new states, increasing the robustness of the particle filter. Results confirm an increase in accuracy compared to state-of-the-art methods.