SiamRPN++D: improved SiamRPN++ using cascaded detector sensing

Vinayak Nageli, Rama Krishna Sai Subrahmanyam Gorthi, Arshad Jamal

Abstract: This paper presents a novel and robust long-term tracking algorithm to address continuous target tracking problems. The continuous target tracking demands handling of correct re-initialization of the lost target when it reappears. The main limitation of the currently popular Siamese class of deep trackers is their inability to re-initialize a target when it is lost for sufficiently long duration or when it re-appears at a location away from the lost location. Most of the Siamese class of deep trackers search for the lost targets in a limited region, close to where it disappears. Hence, they fail in automated re-initialization, tracking resumption and maintaining track after long-term occlusion or tracker-loss. This puts a serious impediment on the current state-of-the-art deep tracker frameworks for many real applications. Here, we propose integration of a lightweight and efficient Cascaded Classifier based detection mechanism with the Siamese trackers for re-initialization of the target. While the proposed approach is generic and applicable to all Siamese class of deep trackers, we have taken SiamRPN++ as a base tracker to illustrate the effectiveness of our tracking framework. The proposition enables Cascaded Classifier based detector to adaptively direct the search region for the base tracker. Extensive experimental results on the well known tracking benchmark datasets such as UAV123, VOT2019, VOT2016, VOT2018 and VOT2018-LT show that the proposed integration significantly improves the performance of the base tracker under occlusion and tracker-loss scenarios. Further, the proposed tracker improves the precision by 1.71% and the recall by 13.98% over the base tracker for the long-term tracking on the VOT2018-LT dataset.