A novel head network and group normalisation help track more accurately

Abstract

AbstractMost existing Siamese Network trackers rely on a predefined anchor box to predict object position. However, they require complicated hyperparameter settings. The authors directly forecast the object boundary by using the fully convolutional network as the head of the tracking network to solve this issue and simplify the application. The end‐to‐end design avoids the setting of hyperparameters and candidate boxes. The authors also discovered that the validation loss decreased less than the training loss throughout Siamese training. The authors changed the normalisation layer from batch normalisation to group normalisation to solve this issue. It solved the problem that the loss function is difficult to decrease and increased training efficiency. Test experiments on the tracking dataset, including VOT2019 and GOT10k, show that the authors’ network outperforms DaSiamRPN and SiamFC regarding precision under the same network size and runs at 24 FPS on an AMD 4800 CPU. It also runs at 306 FPS on a 3090 GPU.