Image sensor communication (ISC), derived from visible light communication (VLC) is an attractive solution for outdoor mobile environments, particularly for intelligent transport systems (ITS). In ITS-ISC, tracking a transmitter in the image plane is critical issue since vehicle vibrations make it difficult to selsct the correct pixels for data reception. Our goal in this study is to develop a precise tracking method. To accomplish this, vehicle vibration modeling and its parameters estimation, i.e., represetative frequencies and their amplitudes for inherent vehicle vibration, and the variance of the Gaussian random process represnting road surface irregularity, are required. In this paper, we measured actual vehicle vibration in a driving situation and determined parameters based on the frequency characteristics. Then, we demonstrate that vehicle vibration that induces transmitter displacement in an image plane can be modeled by only Gaussian random processes that represent road surface irregularity when a high frame rate (e.g., 1000fps) image sensor is used as an ISC receiver. The simplified vehicle vibration model and its parameters are evaluated by numerical analysis and experimental measurement and obtained result shows that the proposed model can reproduce the characteristics of the transmitter displacement sufficiently.

Autonomous driving is not only required to detect pedestrians around vehicles, but also expected to understand the behaviors of pedestrians. Pedestrian body orientation and head orientation are the relevant indicators of the pedestrian intention. This paper proposes an accurate estimation system to recognize the pedestrian body orientation and the pedestrian head orientation from on-board camera and inertial sensors. The proposed system discretizes the body orientation and the head orientation into 16 directions. In order to achieve the accurate orientation estimation, a novel training database is established, which includes strongly labeled data and weakly labeled data. Semi-Supervised Learning method is employed to annotate the weakly labeled data, and to generate the accurate classifier based on the proposed training database. In addition, the temporal constraint and the human physical model constraint are considered in orientation estimation, which are beneficial to the reasonable and stable result of orientation estimation for the pedestrian in image sequences. This estimated result is the orientation in camera space. The comprehension of the pedestrian behavior needs to be conducted in the real world space. Therefore, this paper proposes to model the motion of the host vehicle using inertial sensor, then transforms the estimated orientation from camera space to the real world space by considering the vehicle and pedestrian motion. The represented orientation indicates the behavior of the pedestrian more directly. Finally, a series of experiments demonstrate the effectiveness of the proposed pedestrian orientation system.

The percentage of pedestrian deaths in traffic accidents is on the rise in Japan. In recent years, there have been calls for measures to be introduced to protect vulnerable road users such as pedestrians and cyclists. In this study, a method to detect and track pedestrians using an in-vehicle camera is presented. We improve the technology of detecting pedestrians by using the highly accurate images obtained with a monocular camera. In the detection step, we employ ECoHOG as the feature descriptor; it accumulates the integrated gradient intensities. In the tracking step, we apply an effective motion model using optical flow and the proposed feature descriptor ECoHOG in a tracking-by-detection framework. These techniques were verified using images captured on real roads.

The cellular-communication systems of the future will be required to provide multimedia services to users moving about in a variety of ways (on foot, in automobiles etc.). Different forms of motion have different characteristics. The characterization of the different forms of motion and their effects on telecommunications traffic is important in the planning, design and operation of mobile communication networks. The characterization of the motion of various platform types (inter-city buses, recreational vehicles, freight trucks, and taxis) based on measurements using Global Positioning System is presented in this paper. The measured characteristics of motion are then used to evaluate teletraffic statistics such as cell cross-over rate and cell dwell time by overlaying hypothetical cell systems on the measured loci of vehicles. Self-similarity was discovered in the cell dwell time characteristic of the taxis.