End-to-end feature fusion siamese network for adaptive visual tracking
This addresses the problem of long-term visual tracking for applications like surveillance or robotics, but it is incremental as it builds on existing Siamese network methods.
The paper tackled adaptive visual tracking by proposing an end-to-end feature fusion Siamese network (FF-Siam) that fuses different features to handle changes in object appearance, achieving state-of-the-art performance on Temple-Color, OTB50, and UAV123 benchmarks.
According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long-term tracking task. Motivated by them, we proposed an end-to-end feature fusion framework based on Siamese network, named FF-Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted features are then put into a weight generation layer to obtain the channel weights, which indicate the importance of different feature channels. Both features and the channel weights are utilized in a template generation layer to generate a discriminative template. Finally, the corresponding response maps created by the convolution of the search region features and the template are applied with a fusion layer to obtain the final response map for locating the target. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular Temple-Color, OTB50 and UAV123 benchmarks.