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Mobile robots equipped with an omnidirectional camera have gained a considerable attention over the last decade. Having an entire view of the scene can be very advantageous in numerous applications as all information is stored in a single frame. This paper is primarily concerned with detection of moving objects from optical flow field in cluttered indoor environments, which is necessary for safe navigation and collision avoidance. The algorithm is based on the comparison of the measured optical flow vectors with the generated ones. As depth information is not available, a novel method is proposed which iteratively generates optical flow vectors for different possible real world coordinates of the objects in the scene. This is necessary in order to incorporate motion estimates given by motor encoders. Back-projecting into image is then used to generate synthetic optical flow vectors needed for comparison. The algorithm was tested on a real system and was able to successfully localize a moving object under both artificial and natural lighting. The proposed algorithm can be implemented in real-time on any system with known calibrated model of the omnidirectional sensor and reliable motion estimation.