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In this paper we propose an algorithm for counting moving objects in outdoor environments. Similar to other approaches the proposed algorithm uses a traditional surveillance system approach: background subtraction followed by noise removing, tracking and object labeling. The novelty of the proposed algorithm is that each processing step uses a stereo camera. In fact, the algorithms use both rectified and depth images obtained with a stereo camera. Moving objects are extracted from the scene using a novel background subtraction approach. In outdoor environment the images can be degraded by noise. We considered two types of noise, namely periodic movements due to the wind and the cast shadow. In this paper novel algorithms for detecting these types of noise are proposed. Using them, the noisy pixels are removed. The resulting segmented pixels are grouped together by clustering connected regions and then tracked during their movements. The obtained blobs, which correspond to the moving objects, are thus obtained with high accuracy. The final part of the algorithm is blob classification, which identifies the moving objects. The knowledge of the moving objects can be used to build more complex counting applications than that based on just counting blobs. Two simple applications based on the proposed algorithm are worked out and discussed in this paper, namely one that counts the people moving on the left and on the right of the video scene and one that counts the cars moving in the same way.