To watch 360° videos on normal 2D displays, we need to project the selected part of the 360° image onto the 2D display plane. In this paper, we propose a fully-automated framework for generating content-aware 2D normal-view perspective videos from 360° videos. Especially, we focus on the projection step preserving important image contents and reducing image distortion. Basically, our projection method is based on Pannini projection model. At first, the salient contents such as linear structures and salient regions in the image are preserved by optimizing the single Panini projection model. Then, the multiple Panini projection models at salient regions are interpolated to suppress image distortion globally. Finally, the temporal consistency for image projection is enforced for producing temporally stable normal-view videos. Our proposed projection method does not require any user-interaction and is much faster than previous content-preserving methods. It can be applied to not only images but also videos taking the temporal consistency of projection into account. Experiments on various 360° videos show the superiority of the proposed projection method quantitatively and qualitatively.

Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera movements and thus not always smooth or predictable. To deal with such unexpected camera motion for online 2D MOT, a structural motion constraint between objects has been utilized thanks to its robustness to camera motion. In this paper, we propose a new data association method that effectively exploits structural motion constraints in the presence of large camera motion. In addition, to further improve the robustness of data association against mis-detections and false positives, a novel event aggregation approach is developed to integrate structural constraints in assignment costs for online MOT. Experimental results on a large number of datasets demonstrate the effectiveness of the proposed algorithm for online 2D MOT.

Recently, mobile devices such as smart phones and quad-copters are being equipped with inertial measurement units (IMUs) because of advances in micro-electro-mechanical systems technology. This has increased the importance of IMU- camera fusion for vision-based applications. However, ultralow-cost IMUs take much less accurate measurements than low-cost and high-cost IMUs. This uncertainty degrades the accuracy and reliability of IMU-camera calibration, which is the most important step for IMU-camera fusion technology. In this paper, we propose three effective algorithms for robust IMU- camera calibration with uncertain measurements: boundary constraint, adaptive prediction, and angular velocity constraint. These algorithms incorporate a Bayesian filtering framework to estimate calibration parameters more efficiently. The experimental results on both simulation and real data demonstrated the superiority of the proposed algorithms.