No abstract

Gait analysis is a promising biometric technology to visually and quantitatively analyze an individual’s walking style. In Japan, silhouette-based quantitative gait analyses have been implemented as a forensic tool; however, several challenges remain owing the narrow range of application. One of the yet-unsolved issues pertains to the existence of a ‘slight’ but critical viewing direction difference, which leads to the incorrect judgment in the analyses of a person even when using deep learning-based feature extraction. To alleviate the critical viewing direction difference problem, we developed a novel gait analysis technique involving three components: 3D calibration, gait energy image space registration, and regression of the distance vector. Results of the GUI development and mock appraisal tests indicated that the proposed method can help achieve practical improvements in the forensic science domain.

No abstract

Vision-based object detection using camera sensors is an essential piece of perception for autonomous vehicles. Various combinations of features and models can be applied to increase the quality and the speed of object detection. A wellknown approach uses histograms of oriented gradients (HOG) with deformable models to detect a car in an image [15]. A major challenge of this approach can be found in computational cost introducing a real-time constraint relevant to the real world. In this paper, we present an implementation technique using graphics processing units (GPUs) to accelerate computations of scoring similarity of the input image and the pre-defined models. Our implementation considers the entire program structure as well as the specific algorithm for practical use. We apply the presented technique to the real-world vehicle detection program and demonstrate that our implementation using commodity GPUs can achieve speedups of 3x to 5x in frame-rate over sequential and multithreaded implementations using traditional CPUs.