Object extraction and tracking in a video image is basic technology for many applications, such as video surveillance and robot vision. Many moving object extraction and tracking methods have been proposed. However, they fail when the scenes include illumination change or light reflection. For tracking the moving object robustly, we should consider not only the RGB values of input images but also the shape information of the objects. If the objects' shapes do not change suddenly, matching positions on the cost matrix of exclusive block matching are located nearly on a line. We propose a method for obtaining the correspondence of feature points by imposing a matching position constraint induced by the shape constancy. We demonstrate experimentally that the proposed method achieves robust tracking in various environments.

Since wild animals are causing more accidents and damages, it is important to safely detect them as early as possible. In this paper, we propose two battery-powered wild animal detection nodes based on deep learning that can automatically detect wild animals; the detection information is notified to the people concerned immediately. To use the proposed nodes outdoors where power is not available, we devise power saving techniques for the proposed nodes. For example, deep learning is used to save power by avoiding operations when wild animals are not detected. We evaluate the operation time and the power consumption of the proposed nodes. Then, we evaluate the energy consumption of the proposed nodes. Also, we evaluate the detection range of the proposed nodes, the accuracy of deep learning, and the success rate of communication through field tests to demonstrate that the proposed nodes can be used to detect wild animals outdoors.

Template matching for image sequences captured with a moving camera is very important for several applications such as Robot Vision, SLAM, ITS, and video surveillance systems. However, it is difficult to realize accurate template matching using only visual feature information such as HSV histograms, edge histograms, HOG histograms, and SIFT features, because it is affected by several phenomena such as illumination change, viewpoint change, size change, and noise. In order to realize robust tracking, structure information such as the relative position of each part of the object should be considered. In this paper, we propose a method that considers both visual feature information and structure information. Experiments show that the proposed method realizes robust tracking and determine the relationships between object parts in the scenes and those in the template.

In this paper, starting from the algorithm, a performance- and energy-efficient 3D structure or shape of the Tensor Processing Engine (TPE) for CNN acceleration is systematically searched and evaluated. An optimal accelerator's shape maximizes the number of concurrent MAC operations per clock cycle while minimizes the number of redundant operations. The proposed 3D vector-parallel TPE architecture with an optimal shape can be very efficiently used for considerable CNN acceleration. Due to implemented support of inter-block image data independency, it is possible to use multiple of such TPEs for the additional CNN acceleration. Moreover, it is shown that the proposed TPE can also be uniformly used for acceleration of the different CNN models such as VGG, ResNet, YOLO, and SSD. We also demonstrate that our theoretical efficiency analysis is matched with the result of a real implementation for an SSD model to which a state-of-the-art channel pruning technique is applied.

Ball Grid Array packages in which I/O pins are arranged in a grid array pattern realize a number of connections between chips and a printed circuit board, but it takes much time in manual routing. We propose a fast routing method for 2-layer Ball Grid Array packages that iteratively modifies via assignment. In experiments, in most cases, via assignment and global routing on both of layers in which all nets are realized and the violation of wire congestion on layer 1 is small are speedily obtained.

In this paper, we propose a routing method for 2-layer ball grid array packages that generates a routing pattern satisfying a design rule. In our proposed method, the routing structure on each layer is restricted while keeping most of feasible patterns to efficiently obtain a feasible routing pattern. A routing pattern that satisfies the design rule is formulated as a mixed integer linear programming. In experiments with seven data, we obtain a routing pattern such that satisfies the design rule within a practical time by using a mixed integer linear programming solver.

Ball Grid Array packages in which I/O pins are arranged in a grid array pattern realize a number of connections between chips and PCB, but it takes much time in manual routing. So the demand for automation of package routing is increasing. In this paper, we give the necessary and sufficient condition that all nets can be connected by monotonic routes when a net consists of a finger and a ball and fingers are on the two parallel boundaries of the Ball Grid Array package, and propose a monotonic routing method based on this condition. Moreover, we give a necessary condition and a sufficient condition when fingers are on the two orthogonal boundaries, and propose a monotonic routing method based on the necessary condition.

Electromagnetic field analysis is a time-consuming process, and a method involving the use of an FPGA accelerator is one of the attractive ways to accelerate the analysis; the other method involve the use of CPU and GPU. In this paper, we propose an FPGA accelerator dedicated for a two-dimensional finite-difference time-domain (FDTD) method. This accelerator is based on a two-dimensional single instruction multiple data (SIMD) array architecture. Each processing element (PE) is composed of a six-stage pipeline that is optimized for the FDTD method. Moreover, driving signal generation and impedance termination are also implemented in the hardware. We demonstrate that our accelerator is 11 times faster than existing FPGA accelerators and 9 times faster than parallel computing on the NVIDIA Tesla C2075. As an application of the high-speed FDTD accelerator, the design optimization of a waveguide is shown.

Hardware acceleration is an essential technique for extracting and tracking moving objects in real time. It is desirable to design tracking algorithms such that they are applicable for parallel computations on hardware. Exclusive block matching methods are designed for hardware implementation, and they can realize detailed motion extraction as well as robust moving object tracking. In this study, we develop tracking hardware based on an exclusive block matching method on FPGA. This tracking hardware is based on a two-dimensional systolic array architecture, and can realize robust moving object extraction and tracking at more than 100 fps for QVGA images using the high parallelism of an exclusive block matching method, synchronous shift data transfer, and special circuits to accelerate searching the exclusive correspondence of blocks.

Recently, video surveillance systems have been widely introduced in various places, and protecting the privacy of objects in the scene has been as important as ensuring security. Masking each moving object with a background subtraction method is an effective technique to protect its privacy. However, the background subtraction method is heavily affected by sunshine change, and a redundant masking by over-extraction is inevitable. Such superfluous masking disturbs the quality of video surveillance. In this paper, we propose a moving object masking method combining background subtraction and machine learning based on Real AdaBoost. This method can reduce the superfluous masking while maintaining the reliability of privacy protection. In the experiments, we demonstrate that the proposed method achieves about 78-94% accuracy for classifying superfluous masking regions and moving objects.

Detailed tracking is required for many vision applications. A visual feature-based constraint underlies most conventional motion estimation methods. For example, optical flow methods assume that the brightness of each pixel is constant in two consecutive frames. However, it is difficult to realize accurate extraction and tracking using only visual feature information, because viewpoint changes and inconsistent illumination cause the visual features of some regions of objects to appear different in consecutive frames. A structure-based constraint of objects is also necessary for tracking. In the proposed method, both visual feature matching and structure matching are formulated as a linear assignment problem and then integrated.