Search

Driver inattention is one of the main causes of traffic accidents. To avoid such accidents, advanced driver assistance system that passively monitors the driver’s activities is needed. In this paper, we present a novel method to estimate a head pose from a monocular camera. The proposed algorithm is based on multi-task learning deep neural network that uses a small grayscale image. The network jointly detects multi-view faces and estimates head pose even under poor environment conditions such as illumination change, vibration, large pose change, and occlusion. We also propose a multi-task learning method that does not bias on a specific task with different datasets. Moreover, in order to fertilize training dataset, we establish and release the RCVFace dataset that has accurate head poses. The proposed framework outperforms state-of-the-art approaches quantitatively and qualitatively with an average head pose mean error of less than 4° in real-time. The algorithm applies to driver monitoring system that is crucial for driver safety. [ABSTRACT FROM AUTHOR]

This paper presents a practical means of extrinsic calibration between a camera and a 2D laser sensor, without overlap. In previous calibration methods, the sensors must be able to see a common geometric structure such as a plane or a line. In order to calibrate a non-overlapping camera–laser system, it is necessary to attach an extra sensor, such as a camera or a 3D laser sensor, whose relative poses from both the camera and the 2D laser sensor can be calculated. In this paper, we propose two means of calibrating a non-overlapping camera–laser system directly without an extra sensor. For each method, the initial solution of the relative pose between the camera and the 2D laser sensor is computed by adopting a reasonable assumption about geometric structures. This is then refined via non-linear optimization, even if the assumption is not met perfectly. Both simulation results and experiments using actual data show that the proposed methods provide reliable results compared to the ground truth, as well as similar or better results than those provided by conventional methods. [ABSTRACT FROM AUTHOR]

This paper describes a new methodology for estimating the relative pose between two 2-D lidars. Scanned points of 2-D lidars do not have enough feature information for correspondence matching. For this reason, additional image sensors or artificial landmarks at known locations have been used to find the relative pose. We propose a novel method of estimating the relative pose between 2-D lidars without any additional sensors or artificial landmarks. By scanning two orthogonal planes, we utilize the coplanarity of the scan points on each plane and the orthogonality of the plane normals. Even if we capture planes which are not exactly orthogonal, the method provides good results using nonlinear optimization. Experiments with both synthetic and real data show the validity of the proposed method. We also derive two degenerate cases: one related to plane poses, and the other caused by the relative pose. To the best of our knowledge, this study provides the first solution for the problem. [ABSTRACT FROM PUBLISHER]

Human gaze information has been widely used in various areas, such as medical diagnosis and human–computer interactions (HCI). This study proposes a head pose‐free 3D gaze estimation method using a deep convolutional neural network (DCNN). To infer gaze direction, only a small grayscale image is required without any special devices such as an infrared (IR) illuminator and RGBD sensor. A domain adaptation method to reduce the feature gap between real and synthetic image data is also proposed here. Moreover, a novel synthetic dataset (SynFace) that contains head poses, gaze directions, and facial landmarks is established and released. The proposed method outperforms state‐of‐the‐art methods and achieves a mean error of less than 4○. [ABSTRACT FROM AUTHOR]

We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms. Because most image processing algorithms rely heavily on low-level image features that are based mainly on local gradient information, we consider that gradient quantity can determine the proper exposure level, allowing a camera to capture the important image features in a manner robust to illumination conditions. We then extend this concept to a multi-camera system and present a new control algorithm to achieve both brightness consistency between adjacent cameras and a proper exposure level for each camera. We implement our prototype system with off-the-shelf machine-vision cameras and demonstrate the effectiveness of the proposed algorithms on practical applications, including pedestrian detection, visual odometry, surround-view imaging, panoramic imaging, and stereo matching. [ABSTRACT FROM AUTHOR]