![]() Over the years, increasingly complex pixel-level algorithms have been proposed. As for other PCA-based methods, the initialization process and the update mechanism are not described. Individual pixels are classified as background or foreground using simple image difference thresholding between the current image and the backprojection in the image space of its PCA coefficients. While the PCA model is also trained with time samples, the resulting model accounts for the whole image. In, the authors focus on the PCA reconstruction error. Such a technique is also described in, but it lacks an update mechanism to adapt the block models over time. A block of a new video frame is classified as background if its observed image pattern is close to its reconstructions using PCA projection coefficients of 8-neighboring blocks. A few samples are then collected over time and used to train a Principal Component Analysis (PCA) model for each block. Despite this inconvenience, pixels are aggregated into blocks and each N × N block is processed as an N²-component vector. While this assumption holds most of the time, especially for pixels belonging to the same background object, it becomes problematic for neighboring pixels located at the border of multiple background objects. in is based on the assumption that neighboring blocks of background pixels should follow similar variations over time. By contrast, the method described by Seiki et al. Since perturbations often affect individual pixels, this results in local misclassifications. These techniques relegate entirely to post-processing algorithms the task of adding some form of spatial consistency to their results. Most techniques described in the literature operate on each pixel independently. Section 5↓ concludes the paper.Ģ Review of background subtraction algorithms We show that, even in its simplified form, our algorithm performs better than more sophisticated techniques. We also present a simplified version of our algorithm which requires only one comparison and one byte of memory per pixel this is the absolute minimum in terms of comparisons and memory for any background subtraction technique. Section 4↓ discusses experimental results including comparisons with other state-of-the-art algorithms and computational performance. Section 3↓ describes our technique and details our major innovations: the background model, the initialization process, and the update mechanism. We have implemented some of these algorithms in order to compare them with our method. This review presents the major frameworks developed for background subtraction and highlights their respective advantages. In Section 2↓, we extensively review the literature of background subtraction algorithms. ![]() This method has been briefly described in and in a patent. In this paper, we present a universal method for background subtraction. ![]() An alternative definition for the background is that it corresponds to a reference frame with values visible most of the time, that is with the highest appearance probability, but this kind of framework is not straightforward to use in practice. Clearly a ghost is irrelevant for motion interpretation and has to be discarded. Note that when a static object starts moving, a background subtraction algorithm detects the object in motion as well as a hole left behind in the background (referred to as a ghost). The purpose of a background subtraction algorithm is therefore to distinguish moving objects (hereafter referred to as the foreground) from static, or slow moving, parts of the scene (called background). This is the basic principle of background subtraction, which can be formulated as a technique that builds a model of a background and compares this model with the current frame in order to detect zones where a significant difference occurs. Simple motion detection algorithms compare a static background frame with the current frame of a video scene, pixel by pixel. In order to detect, segment, and track objects automatically in videos, several approaches are possible. But this growth has resulted in a huge augmentation of data, meaning that the data are impossible either to store or to handle manually. The number of cameras available worldwide has increased dramatically over the last decade.
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