Gianni Allebosch

EFIC: Edge Based Foreground Background Segmentation and Interior Classification for Dynamic Camera Viewpoints

Foreground background segmentation algorithms attempt to separate interesting or changing regions from the background in video sequences. Foreground detection is obviously more difficult when the camera viewpoint changes dynamically, such as when the camera undergoes a panning or tilting motion. In this paper, we propose an edge based foreground background estimation method, which can automatically detect and compensate for camera viewpoint changes. We will show that this method significantly outperforms state-of-the-art algorithms for the panning sequences in the ChangeDetection.NET 2014 dataset, while still performing well in the other categories.

C-EFIC: Color and Edge Based Foreground Background Segmentation with Interior Classification

The detection of foreground regions in video streams is an essential part of many computer vision algorithms. Considerable contributions were made to this field over the past years. However, varying illumination circumstances and changing camera viewpoints provide major challenges for all available algorithms. In this paper, a robust foreground background segmentation algorithm is proposed. Both Local Ternary Pattern based edge descriptors and RGB color information are used to classify individual pixels. Furthermore, camera viewpoints are detected and compensated for. We will show that this algorithm is able to handle challenging conditions and achieves state-of-the-art results on the comprehensive ChangeDetection.NET 2014 dataset.

Edge based foreground background estimation with interior/exterior classification

Foreground background estimation is an essential task in many video analysis applications. Considerable improvements are still possible, especially concerning light condition invariance. In this paper, we propose a novel algorithm which attends to this requirement. We use modified Local Ternary Pattern (LTP) descriptors to find likely strong and stable “foreground gradient” locations. The proposed algorithm then classifies pixels as interior or exterior, using a shortest path algorithm, which proves to be robust against contour gaps.

High performance multi-camera tracking using shapes-from-silhouettes and occlusion removal

Reliable indoor tracking of objects and persons is still a major challenge in computer vision. As GPS is unavailable indoors, other methods have to be used. Multi-camera systems using colour cameras is one approach to tackle this problem. In this paper we will present a method based on shapes-from-silhouettes where the foreground/background segmentation videos are produced with state of the art methods. We will show that our tracker outperforms all the other trackers we evaluated and obtains an accuracy of 97.89% within 50 cm from the ground truth position on the proposed dataset.

Foreground Background Segmentation in Front of Changing Footage on a Video Screen

In this paper, a robust approach for detecting foreground objects moving in front of a video screen is presented. The proposed method constructs a background model for every image shown on the screen, assuming these images are known up to an appearance transformation. This transformation is guided by a color mapping function, constructed in the beginning of the sequence. The foreground object is then segmented at runtime by comparing the input from the camera with a color mapped representation of the background image, by analysing both direct color and edge feature differences. The method is tested on challenging sequences, where the background screen displays photo-realistic videos. It is shown that the proposed method is able to produce accurate foreground masks, with obtained \(F_1\)-scores ranging from 85.61% to 90.74% on our dataset.

Body Related Occupancy Maps for Human Action Recognition

This paper introduces a novel spatial feature for human action recognition and analysis. The positions and orientations of body joints relative to a reference point are used to build an occupancy map of the 3D space that was occupied during the action execution. The joint data is acquired with the Microsoft Kinect v2 sensor and undergoes a pose invariant normalization process to eliminate body differences between different persons. The body related occupancy map (BROM) and its 2D views are used as feature input for a random forest classifier. The approach is tested on a self-captured database of 23 human actions for game-play. On this database a classification with an F1-score of 0.84 is achieved for the front view of the BROM from the complete skeleton.

C-EFIC: Color and edge based foreground background segmentation with interior classification

The detection of foreground regions in video streams is an essential part of many computer vision algorithms. Considerable contributions were made to this field over the past years. However, varying illumination circumstances and changing camera viewpoints provide major challenges for all available algorithms. In this paper, a robust foreground background segmentation algorithm is proposed. Both Local Ternary Pattern based edge descriptors and RGB color information are used to classify individual pixels. Furthermore, camera viewpoints are detected and compensated for. We will show that this algorithm is able to handle challenging conditions and achieves state-of-the-art results on the comprehensive ChangeDetection.NET 2014 dataset.