  Convolutional neural networks (CNN) allow achieving the highest accuracy for
the task of object detection in images. Major challenges in further development
of object detectors are false-positive detections and high demand of processing
power. In this paper, we propose an approach to object detection which makes it
possible to reduce the number of false-positive detections by processing only
moving objects and reduce the required processing power for algorithm
inference. The proposed approach is a modification of CNN already trained for
object detection task. This method can be used to improve the accuracy of an
existing system by applying minor changes to the algorithm. The efficiency of
the proposed approach was demonstrated on the open dataset "CDNet2014
pedestrian". The implementation of the method proposed in the article is
available on the GitHub: https://github.com/david-svitov/AmphibianDetector
