We propose a pixel-level out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. The PixOOD consists of two main parts - in-distribution data model and decision strategy estimator. In order to model the complex intraclass variability of the in-distribution data at the pixel-level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through (stochastic) gradient descent techniques. Furthermore, we propose two models for estimating decision strategy, perclass and unified calibration models, each suitable for different applications. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD.