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.

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.

This paper describes an affine motion estimation processor for real-time video segmentation. The processor estimates the dominant motion of a target region with affine parameters. The processor is based on the Pseudo-M-estimator algorithm. Introduction of an image division method and a binary weight method to the original algorithm reduces data traffic and hardware costs. A pixel sampling method is proposed that reduces the clock frequency by 50%. The pixel pipeline architecture and a frame overlap method double throughput. The processor was prototyped on an FPGA; its function and performance were subsequently verified. It was also implemented as an ASIC. The core size is 5.05.0 mm2 in 0.18 µm process, standard cell technology. The ASIC can accommodate a VGA 30 fps video with 120 MHz clock frequency.

Face motion is composed of rigid motion and non-rigid motion. The rigid motion occurs from movements of the human head and the non-rigid motion derives from human's facial expression. In this paper, we present a technique for estimating these rigid/non-rigid motions of the human face simultaneously. First, we test whether the face motion is rigid. If it is rigid motion, we estimate the translation and rotation parameters over image sequences. Otherwise, the non-rigid motion parameters based on the spring-mass-damper (SMD) model are estimated using optical flow. We separate the rigid motion parameters explicitly from the non-rigid parameters for parameters de-coupling, so that we can achieve the face motion estimation more accurately and more efficiently. We will describe the details of our methods and show their efficacy with experiments.

This paper presents a new approach to shape and motion estimation based on geometric primitives and relations in a model-based framework. A description of a scene in terms of structured geometric elements sharing relationships allows to derive a parametric model with Euclidian constraints, and a camera model is also proposed to reduce the problem dimensionality. It leads to a sequential MAP estimation, that gives accurate and comprehensible results on real images.

A new motion field segmentation algorithm under the 8-parameters motion model is presented which uses a multipass iterative region-refining techinique. The iterative region-refining module consists of a seed block detection and subsequent region-refining iterations. An initial estimate of an object motion is provided in the seed block detection process. This initial estimate is iteratively updated and approaches to a reliable mapping parameter set in region-refining process. A multipass composition of the module makes it possible to detect multiple motions in a scene. Our simulation results confirm that the proposed method successfully partitions an image into independently moving objects with allowable computation time.