Image-based Head Pose Estimation (HPE) from an arbitrary view is still challenging due to the complex imaging conditions as well as the intrinsic and extrinsic property of the faces. Different from existing HPE methods combining additional cues or tasks, this paper solves the HPE problem by relieving problem complexity. Our method integrates the deep Task-Simplification oriented Image Regularization (TSIR) module with the Anchor-Guided Pose Estimation (AGPE) module, and formulate the HPE problem into a unified end-to-end learning framework. In this paper, we define anchors as images that strictly obey the “gravity rule in camera”, which follows the assumption that camera coordinate of the vertical axis should always be consistent with that of the local head coordinate. We formulate image pair as the regularized image produced by TSIR along with its anchor counterpart, both of which are fed into the AGPE module for estimating fine-grained head poses. This paper also proposes an Anchor-Guided Pairwise Loss (AGPL), which describes the interdependent relevance of poses between each pair of images. The proposed method is evaluated and validated with sufficient experiments which show its effectiveness. Comprehensive experiments show that our approach outperforms the state-of-the-art image-based methods on both indoor and outdoor datasets.

In recent years, unmanned aerial vehicles (UAVs) have rapidly developed, but the illegal use of UAVs by civilians has resulted in disorder and security risks and has increasingly triggered the community concern and worry. Therefore, the monitoring and recycling of UAVs in key regions are of great significance. This paper presents novel panoramic UAV surveillance and autonomous recycling system that is based on the unique structure-free fisheye camera array and has the capability of real-time UAV detection, 3D localization, tracking, and recycling capacity over a very wide field of view. The main characteristics of this paper include the following: 1) constructing a structure-free camera-array-based panoramic UAV surveillance and recycling system; 2) designing a robust dynamic near-infrared laser-source-based self-calibration algorithm for large-scale arbitrary layout of the camera array; and 3) presenting a set of UAV detection, 3D localization, tracking, and autonomous recycling algorithms based on a fish-eye camera array. The system has been tested in various challenging scenarios, including multiple UAVs with significant appearance and scale changes and even poor weather conditions. The extensive experimental results analyzed both qualitatively and quantitatively, and the analysis of the time performance demonstrate the robustness and effectiveness of the proposed system. In addition, we successfully conduct a recycling and landing experiment with the Parrot Beobop.