Human pose estimation from a monocular image has attracted lots of interest due to its huge potential application in many areas. The performance of 2D human pose estimation has been improved a lot with the emergence of deep convolutional neural network. In contrast, the recovery of 3D human pose from an 2D pose is still a challenging problem. Currently, most of the methods try to learn a universal map, which can be applied for all human poses in any viewpoints. However, due to the large variety of human poses and camera viewpoints, it is very difficult to learn a such universal mapping from current datasets for 3D pose estimation. Instead of learning a universal map, we propose to learn an adaptive viewpoint transformation module, which transforms the 2D human pose to a more suitable viewpoint for recovering the 3D human pose. Specifically, our transformation module takes a 2D pose as input and predicts the transformation parameters. Rather than some hand-crafted criteria, this module is directly learned from the datasets and depends on the input 2D pose in testing phrase. Then the 3D pose is recovered from this transformed 2D pose. Since the difficulty of 3D pose recovery becomes smaller, we can obtain more accurate estimation results. Experiments on Human3.6M and MPII datasets show that the proposed adaptive viewpoint transformation can improve the performance of 3D human pose estimation. INDEX TERMS 3D human pose estimation, adaptive viewpoint transformation, deep convolutional neural network

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BACKGROUND There is a certain incidence of pituitary adenomas coexisting with intracranial aneurysms, but a concurrent therapeutic strategy of tumor removal and aneurysm clipping via endoscopic endonasal approach is rarely reported. The indications and limitations of endoscopic endonasal approach surgery for this type of lesions are worth discussing. OBSERVATIONS The case of a pituitary tumor coexisting with a paraclinoid aneurysm was reviewed. Using an endoscopic endonasal approach, the pituitary adenoma was completely excised with extrapseudocapsular separation technique, the aneurysm was clipped at the same time, and the skull base defect was reconstructed in multilayer fashion. No tumor recurrence was found, and aneurysm clipping was complete at the 6-month follow-up after surgery. LESSONS For patients harboring a pituitary adenoma with a selected paraclinoid aneurysm, simultaneous tumor resection and aneurysm clipping via endoscopic endonasal approach are feasible. This strategy has the advantages of saving medical resources, promoting the patient’s rapid postoperative recovery, and reducing possible antiplatelet therapy after interventional therapy. However, surgery needs to strictly follow the indications in experienced hands, and the therapeutic effect needs to be verified by more cases and longer follow-up results.

With the maturity of Unmanned Aerial Vehicle (UAV) technology and the development of Industrial Internet of Things, drones have become an indispensable part of intelligent transportation systems. Due to the absence of an effective identification scheme, most commercial drones suffer from impersonation attacks during their flight procedure. Some pioneering works have already attempted to validate the pilot’s legal status at the beginning and during the flight time. However, the off-the-shelf pilot identification scheme can not adapt to the dynamic pilot membership management due to a lack of extensibility. To address this challenge, we propose an incremental learning-based drone pilot identification scheme to protect drones from impersonation attacks. By utilizing the pilot temporal operational behavioral traits, the proposed identification scheme could validate pilot legal status and dynamically adapt newly registered pilots into a well-constructed identification scheme for dynamic pilot membership management. After systemic experiments, the proposed scheme was capable of achieving the best average identification accuracy with 95.71% on P450 and 94.23% on S500. With the number of registered pilots being increased, the proposed scheme still maintains high identification performance for the newly added and the previously registered pilots. Owing to the minimal system overhead, this identification scheme demonstrates high potential to protect drones from impersonation attacks.