Robust Object Tracking via Long-Range Spatial Representation and Local Feature Enhancement
Jun Wang, Bingfei Chai, Lingtao Zhou, Yuanyun Wang · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Despite the excellent performance achieved by Siamese tracking in most scenarios, its performance remains unsatisfactory in some scenarios. Traditional convolutional operations tend to use a fixed-size convolution kernel, which results in a small receptive field; thus, the model focuses only on local target features, keeping the model from considering global dependencies. Intercorrelation operations also do not explicitly model and capture information about local targets’ corner regions, which prevents the model from considering critical information about target edges and reduces the accuracy of locating the target. In this paper, we propose a feature extraction subnetwork based on a long-range spatial representation module that captures long-range spatial dependencies between the foreground and background. The network allows the model to learn more discriminative feature representations. We also construct a feature fusion network based on a local feature enhancement module that considers features contained in local targets’ corner regions more strongly. The proposed model can learn more comprehensive and detailed feature representations that lead to more accurate tracking. The proposed tracker is compared with SOTA trackers on six tracking datasets, and an average tracking speed of 45 FPS is achieved. Especially, it achieves 86.5% precision and 67.2% success rate on the UAV123 dataset with 40 FPS.