Monitoring animal location can be a valuable tool in research and for practical applications, such as health or pasture management. Although GPS is commonly used, other solutions are available, such as RFID or image analysis. Image analysis is a non-invasive technique that has been proved to be useful to monitor animal location, as well as animal behaviour. Most, if not all, applications of image analysis for the continuous monitoring of farm animals have been developed with top-view cameras in indoor conditions. In this article, we develop a framework that combines low cost time lapse cameras, machine learning, and image registration, in order to monitor the location of animals in a pasture. We tested our framework by monitoring two flocks of goats under farm-like conditions. One time lapse camera was able to monitor an area of approximately 20 m by 20 m, and several cameras were combined to monitor the entire pasture. The precision and sensitivity of this method for automatic animal detection was estimated to be 90% and 84.5%, but the results can vary with the layout of the pasture. For example, goats were hardly detectable in front of a natural hedge, which appears dark in the image. In addition, any unwanted elements in the pasture can increase the false positive detection rate. Small animals, such as kids, were also difficult to detect in some cases, as they can be smaller than the weeds. With all the tested layouts, the sensitivity varies from 70.7% to 94.8% and the precision varies from 83.8% to 95.6%. The spatial accurracy of the method was also estimated. At a distance of 10 m, the maximal accuracy is approximately 56 cm, whereas the maximal accuracy is equal to 116 cm when the animals are at a distance of 20 m from the camera. This study shows that image analysis can be an interesting alternative to GPS with comparable accuracy and significantly lower cost.