Real-time Stable Texture Regions Extraction for Motion-Based Object Segmentation
Dubravko R. Culibrk, Borislav Antić, Vladimir S. Crnojevic · 2009
Although used extensively for object recognition, texture has for the most part been ignored as a feature used for background modelling and object segmentation. The complexity of working with texture descriptors for segmentation in videos is two-fold: the descriptive features cannot be calculated in real time and features extracted based on arbitrarily chosen regions or blocks in the frame are not stable enough to allow for building models sufficiently accurate, yet simple enough to be used for real-time segmentation. The paper proposes an approach that can be used to detect regions of texture, stable enough to be modelled using probabilistic models commonly used for foreground segmentation. Based on the evaluated stable texture regions, a discriminative texture descriptor is proposed that can be evaluated in real time. When video is captured from a stationary camera, the background is expected to be stationary to a degree and an adaptive model can be built to serve as basis for segmentation. For reasons of efficiency most adaptive models are learnt on a per pixel basis [4]. Joint domain-space modelling [3], as well as modelling of the sequence at two different scales, has been suggested as a way to enhance the segmentation and escape the limitations of single-pixel-based models[1]. The improved approaches still fail to consider the texture properties of the objects in the background and suffer from the noise introduced by neighboring objects being modelled as one. Texture of the background can be used to build models in a more informed way. Large stable regions in the texture of the background allow for larger scale modelling and more precise extraction of texture related features. Conversely, unstable regions in terms of texture correspond to transitions between different objects in the background, and the features should be extracted for each of the objects separately. An approach to background modelling and foreground segmentation derived form these principles is proposed in the paper. In each frame of the sequence stable texture regions are determined, texture descriptor (feature) values extracted using the stable regions information and used to model the background and segment the foreground using standard probabilistic approaches. The process is illustrated in Fig. 1, where the brighter patches in the stability map correspond to more stable regions, in terms of texture. Once the size of the stable-texture region at each location is determined, arbitrary descriptive features can be extracted from the frames of the sequence. In addition, one can choose any of a number of probabilistic methods [4][2] to learn the statistics of the features. The size of the stable texture regions can be determined online, using integral images [6]. Observe that: