BEST PAPER AWARDS

Date: Sep 29 (Tuesday)
Time: 13:00-14:30
Session: G4
Room: Lyon D
Candidate List:

Final Placement	Title/Author/Abstract
First Place	Efficient Finger-Vein Recognition System Based on Fast Binary Robust Independent Elementary Feature Combined with Multi-Image Quality Assessment Verification Jing-Ming Guo, Chong-Sheng Wu and Li-Ying Chang (National Taiwan University of Science and Technology, Taiwan) This paper proposes an efficient finger-vein recognition system that uses a binary robust invariant elementary feature that uses features from accelerated segment test feature points and an adaptive thresholding strategy (FBRIEF). This recognition structure allows an efficient feature points matching using FBRIEF and rigorous verification using the MIQA process. Experimental results show that the best EER performance is 0.13% and 0.69%, using homemade and public datasets.
Second Place	Comparison of Stabilization Control in Remote Control System with Haptics Lu Chen, Limin Wen and Yutaka Ishibashi (Nagoya Institute of Technology, Japan); Pingguo Huang (Gifu Shotoku Gakuen University, Japan); Yuichiro Tateiwa (Nagoya Institute of Technology, Japan) This paper makes a comparison of two types of stabilization control in a remote control system with haptics, By using a haptic interface devise, a user manipulates another haptic interface device at a remote location while watching video. One is the adaptive viscosity control, and the other is the stabilization control by viscosity. By QoE (Quality of Experience) assessment, we clarify which type of control is better than the other type in terms of the operability of haptic interface device. As a result, we demonstrate that the adaptive viscosity control is better than the stabilization control by viscosity when the network delay is short, and the latter is superior to the former when the network delay is long. However, when the network delay is too long, they are almost the same.
Third Place	An Estimation Method of Visibility Level on Winter Road Based on Multiple Features in CCTV Images Shotaro Kawata, Sho Takahashi and Toru Hagiwara (Hokkaido University, Japan) Since drivers acquire a lot of information with eyes, the estimation of the level of visibility (visibility level) on the winter roads contributes toward enhancing traffic safety. Therefore, this paper proposes a method for estimating the visibility level on the winter roads from closed-circuit television (CCTV) images. In the proposed method, the visibility level of each image is estimated based on features acquired via Fourier transform and Convolutional Neural Network (CNN). Specifically, by constructing support vector machines (SVMs) for each feature, the probabilities of the visibility level are calculated. Furthermore, based on comparison of calculated probabilities of SVMs, the proposed method estimates the visibility level accurately. Experimental results show the effectiveness of the proposed method.
Honorable Mention	Important Scene Detection Based on Anomaly Detection using Long Short-Term Memory for Baseball Highlight Generation Kaito Hirasawa, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama (Hokkaido University, Japan) This paper presents an important scene detection method based on anomaly detection using a Long Short-Term Memory (LSTM) for baseball highlight generation. In order to deal with multi-view time series features calculated from tweets and videos, we adopt an anomaly detection method using LSTM. LSTM which can maintain a long-term memory is effective for training such features. Introduction of LSTM into important scene detection of baseball videos is the biggest contribution of this paper. Experimental results show high detection performance by our method.
Honorable Mention	Multi-User ACO-OFDM Based Optical Wireless Communications Cheng-Yuan Chang, Shih-Chang Chien and Tsai-Che Chen (National United University, Taiwan) In this paper, a new asymmetrically clipped optical orthogonal frequency-division multiplexing (ACO-OFDM) based optical wireless communication system which uses code-division multiple-access (CDMA) technique to satisfy the requirement of the multi-user transmission is proposed. In the proposed system, each user is assigned one of modified maximum length sequence (modified m-sequence) which is mainly constructed by performing both cyclic-shifted copy and zero-padding operations on the traditional m-sequence, as the spreading sequence for supporting the (synchronous) downlink transmission of the system.
Honorable Mention	Multi-Target Detection and Tracking with Semantic Segmentation by Using LiDAR Sensor Jian-Jie Sun and Cheng-Ming Huang (National Taipei University of Technology, Taiwan) This paper constructs a 3D multi-target semantic segmentation, detection and tracking framework by using the point cloud data captured from a LiDAR sensor in the outdoor traffic scene. This paper proposes the 3D multi-target detection by using scene segmentation results of PointNet++ to improve the loss of point cloud data when extracting the spatial features of point cloud data after it converting to the voxel space. The spatial features and the scene segmentation results are also represented as a 2D data by bird's-eye view in which the objects does not overlap in the traffic scene. Since the processing time of detection system is much slower than the data acquisition time. The detected target in the point clouds at each time instant are directly tracked by utilizing the local detection with the intersection-over-union ratio in a prediction region.