This work proposes an embedded system to detect pedestrians using reconfigurable computing making the image acquisition through a mono-camera attached to a vehicle in an urban environment. This work is motivated by the need to reduce the number of traffic accidents, even with government support, each year hundreds of people become victims thus bringing great damage to the economy. As a result, there is also a global concern of scientists to promote economically viable solutions that will contribute to reducing these accidents. A significant issue is related to the speed of response of the human brain to recognize and or to make decisions in situations of danger. This feature generates a demand for technological solutions aimed at helping people to drive vehicles in several respects. The system hardware was developed in FPGA and divided into interconnected blocks. First, for the pretreatment of the video, was built a block for data conversion from the camera to grayscale, then a simplified block for vertical stabilization dynamic video. To detection, two blocks were built, one for binary motion detection and one for a BLOB detection. To classify, was built one block to identify the size of the object in motion by the proportionality and making the selection. The tests in real environment of this system showed great results for a maximum speed of 30 km / h.