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The automatic detection and tracking of general objects (like persons,animals or cars), text and logos in a video is crucial for many videounderstanding tasks, and usually real-time processing as required. We proposeOmniTrack, an efficient and robust algorithm which is able to automaticallydetect and track objects, text as well as brand logos in real-time. It combinesa powerful deep learning based object detector (YoloV3) with high-qualityoptical flow methods. Based on the reference YoloV3 C++ implementation, we didsome important performance optimizations which will be described. The majorsteps in the training procedure for the combined detector for text and logowill be presented. We will describe then the OmniTrack algorithm, consisting ofthe phases preprocessing, feature calculation, prediction, matching and update.Several performance optimizations have been implemented there as well, likedoing the object detection and optical flow calculation asynchronously.Experiments show that the proposed algorithm runs in real-time for standarddefinition ($720x576$) video on a PC with a Quadro RTX 5000 GPU.

OmniTrack: Real-time detection and tracking of objects, text and logos in video