Online intervention siamese tracking

Abstract

Online target update excels in helping a visual tracking algorithm adapt to variations in target appearance during inference, and thus is preferred by recent advances. However, this technique typically operates in a heuristic manner by updating numerous network parameters with large amounts of online-collected data. This poses a distinct challenge: can the online target update be effectively executed without using the aforementioned approach? To this end, we propose a novel target feature update scheme to reduce the need for tedious data collection and computation-intensive parameter updates. This scheme operates on the principle of causal intervention and is just as effective as default parameter updates in visual tracking. Besides, we explore a novel video-specific target label to capture the context of a specific target in video frames for feature discrimination. This makes target features better fit for appearance changes. Such schemes together with the off-shelf pre-trained classification backbone form a novel online intervention siamese tracker (OIS). When equipped with an unsupervised pre-trained backbone, OIS outperforms current state-of-the-art unsupervised trackers on the OTB and VOT. When exploiting a supervised trained backbone, it competes with typical supervised trackers trained on massive offline training and online tracking data.