The automatic detection and tracking of general objects (like persons,\nanimals or cars), text and logos in a video is crucial for many video\nunderstanding tasks, and usually real-time processing as required. We propose\nOmniTrack, an efficient and robust algorithm which is able to automatically\ndetect and track objects, text as well as brand logos in real-time. It combines\na powerful deep learning based object detector (YoloV3) with high-quality\noptical flow methods. Based on the reference YoloV3 C++ implementation, we did\nsome important performance optimizations which will be described. The major\nsteps in the training procedure for the combined detector for text and logo\nwill be presented. We will describe then the OmniTrack algorithm, consisting of\nthe phases preprocessing, feature calculation, prediction, matching and update.\nSeveral performance optimizations have been implemented there as well, like\ndoing the object detection and optical flow calculation asynchronously.\nExperiments show that the proposed algorithm runs in real-time for standard\ndefinition ($720x576$) video on a PC with a Quadro RTX 5000 GPU.\n