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Real-time object pose estimation and tracking is challenging but essential for emerging augmented reality (AR) applications. In general, state-of-the-art methods address this problem using deep neural networks which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where real-world applications usually take place. In addition, head-mounted displays such as AR glasses require at least 90~FPS to avoid motion sickness, which further complicates the problem. We propose a flexible-frame-rate object pose estimation and tracking system for mobile devices. It is a monocular visual-inertial-based system with a client-server architecture. Inertial measurement unit (IMU) pose propagation is performed on the client side for high speed tracking, and RGB image-based 3D pose estimation is performed on the server side to obtain accurate poses, after which the pose is sent to the client side for visual-inertial fusion, where we propose a bias self-correction mechanism to reduce drift. We also propose a pose inspection algorithm to detect tracking failures and incorrect pose estimation. Connected by high-speed networking, our system supports flexible frame rates up to 120 FPS and guarantees high precision and real-time tracking on low-end devices. Both simulations and real world experiments show that our method achieves accurate and robust object tracking.

Additional file 1 : Supplemental Table 1. Grade 3/4 adverse event.

Supplemental material, sj-docx-1-tam-10.1177_17588359221099401 for Durable objective response to sorafenib and role of sequential treatment in unresectable hepatocellular carcinoma by Kuo-Wei Huang, Pei-Chang Lee, Yee Chao, Chien-Wei Su, I-Cheng Lee, Keng-Hsin Lan, Chi-Jen Chu, Yi-Ping Hung, San-Chi Chen, Ming-Chih Hou and Yi-Hsiang Huang in Therapeutic Advances in Medical Oncology

Supplemental material, sj-docx-1-tam-10.1177_17588359221099401 for Durable objective response to sorafenib and role of sequential treatment in unresectable hepatocellular carcinoma by Kuo-Wei Huang, Pei-Chang Lee, Yee Chao, Chien-Wei Su, I-Cheng Lee, Keng-Hsin Lan, Chi-Jen Chu, Yi-Ping Hung, San-Chi Chen, Ming-Chih Hou and Yi-Hsiang Huang in Therapeutic Advances in Medical Oncology

This paper aims at recognizing partially observed human actions in videos. Action videos acquired in uncontrolled environments often contain corrupt frames, which make actions partially observed. Furthermore, these frames can last for arbitrary lengths of time and appear irregularly. They are inconsistent with training data and degrade the performance of pre-trained action recognition systems. We present an approach to address this issue. For each training and testing actions, we divide it into segments and explore the mutual dependency between temporal segments. This property states that the similarity of two actions at one segment often implies their similarity at another. We augment each segment with extra alternatives retrieved from training data. The augmentation algorithm is designed in a way where a few alternatives are good enough to replace the original segment where corrupt frames occur. Our approach is developed upon hidden conditional random fields and leverages the flexibility of hidden variables for uncertainty handling. It turns out that our approach integrates corrupt segment detection and alternative selection into the process of prediction, and can recognize partially observed actions more accurately. It is evaluated on both fully observed actions and partially observed ones with either synthetic or real corrupt frames. The experimental results manifest its general applicability and superior performance, especially when corrupt frames are present in the action videos.

This paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. The mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the illumination conditions and the pose of the subject for the video. To conduct this evaluation, a part of a database captured during the “Mobile Biometry” (MOBIO) European Project was used. In total there were nine participants to the evaluation who submitted a face verification system and five participants who submitted speaker verification systems.