IOU – Siamtrack: IOU Guided Siamese Network For Visual Object Tracking

Recently deep learning-based Siamese networks with region proposals for visual object tracking are becoming popular. These frameworks, while testing, perform extra computations on the output of a trained network to predict the bounding box (bbox). This process hinders end-to-end training of the above class of networks and hampers the precise estimation of the bbox in testing. In this paper, we propose a framework close to the Siamese class of networks, but guided by Intersection Over Union (IOU) to predict precise bbox directly in the image space rather than at the feature space. To maximise the IOU of predicted bbox with respect to ground truth, we introduce a new module and corresponding loss function in training the network. The proposed approach enables end-to-end training and testing under similar lines, circumventing the typical bottleneck of the existing Siamese trackers. When evaluated on VOT2018 and GOT-10k tracking benchmarks, the proposed approach outperformed the base approach by more than 10% in terms of average overlap and compares favourably to state-of-the-art methods.