Siamese network is successfully applied in object tracking. Most of the existing Siamese tracking methods extract template features in the first frame, which will cause the tracker to ignore the appearance change of the target in the subsequent video. In this paper, we propose a tracker based on foreground adaptive bounding box and motion state redetection. The tracker infers the reliability of tracking by the motion pattern of the bounding box. When an anomaly is detected, the tracker will redetect using the continuously updated template. Furthermore, our tracker employs an adaptive bounding box to avoid the effects of inaccurate rotation of the bounding box. The results on the VOT2018 dataset show that our tracker achieves stronger robustness and higher accuracy, providing superior performance compared to the current state-of-the-art trackers.
This paper presents an effective background modeling method that incorporates adaptive mechanism for the dynamic background. Each pixel in the background model is defined by a history of the N most recent image values at each pixel. It then compares the model with the current pixel value to determine whether or not the pixel belongs to the background using the decision threshold. We design the Time-spatial dynamic feature (TSD feature) innovatively to describe the dynamic background. According to the TSD feature, the decision threshes can be adjusted adaptively with feedback loops that overcome global threshold influence for dynamic background. Updating the background model is essential in order to account for changes in the background, such as moving background objects and lighting changes. The update rate in the background model also can be adjusted adaptively with the background changes based on the TSD feature. The experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods on dynamic background video sequences.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.