  Despite the dynamic development of computer vision algorithms, the
implementation of perception and control systems for autonomous vehicles such
as drones and self-driving cars still poses many challenges. A video stream
captured by traditional cameras is often prone to problems such as motion blur
or degraded image quality due to challenging lighting conditions. In addition,
the frame rate - typically 30 or 60 frames per second - can be a limiting
factor in certain scenarios. Event cameras (DVS -- Dynamic Vision Sensor) are a
potentially interesting technology to address the above mentioned problems. In
this paper, we compare two methods of processing event data by means of deep
learning for the task of pedestrian detection. We used a representation in the
form of video frames, convolutional neural networks and asynchronous sparse
convolutional neural networks. The results obtained illustrate the potential of
event cameras and allow the evaluation of the accuracy and efficiency of the
methods used for high-resolution (1280 x 720 pixels) footage.
