SiamReID: Confuser Aware Siamese Tracker with Re-identification Feature
This addresses tracking challenges in aerial imagery for applications like UAV surveillance, but it is incremental as it builds on existing Siamese trackers.
The paper tackled the problem of Siamese trackers struggling with similar-looking confusers during prolonged occlusions in aerial imagery, and the result was SiamReID, which achieved state-of-the-art performance on the UAVDT benchmark.
Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are prevalent in aerial imagery and create challenging conditions due to prolonged occlusions where the tracker object re-appears under different pose and illumination. Our work proposes SiamReID, a novel re-identification framework for Siamese trackers, that incorporates confuser rejection during prolonged occlusions and is well-suited for aerial tracking. The re-identification feature is trained using both triplet loss and a class balanced loss. Our approach achieves state-of-the-art performance in the UAVDT single object tracking benchmark.