CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems.

In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the vehicle BFQ using vehicle-mounted cameras, few studies have been conducted using surveillance cameras. Therefore, this paper conducts a comparative study on various approaches for detecting the vehicle BFQ in surveillance camera environments. Three approaches were selected for comparison, including corner-based, position/size/angle-based, and line-based. For comparison, this paper suggests a way to implement the vehicle BFQ detectors by simply adding extra heads to one of the most widely used real-time object detectors, YOLO. In experiments, it was shown that the vehicle BFQ can be adequately detected by using the suggested implementation, and the three approaches were quantitatively evaluated, compared, and analyzed.

Simplification of Deep Neural Network-Based Object Detector for Real-Time Edge Computing.

This paper presents a method for simplifying and quantizing a deep neural network (DNN)-based object detector to embed it into a real-time edge device. For network simplification, this paper compares five methods for applying channel pruning to a residual block because special care must be taken regarding the number of channels when summing two feature maps. Based on the comparison in terms of detection performance, parameter number, computational complexity, and processing time, this paper discovers the most satisfying method on the edge device. For network quantization, this paper compares post-training quantization (PTQ) and quantization-aware training (QAT) using two datasets with different detection difficulties. This comparison shows that both approaches are recommended in the case of the easy-to-detect dataset, but QAT is preferable in the case of the difficult-to-detect dataset. Through experiments, this paper shows that the proposed method can effectively embed the DNN-based object detector into an edge device equipped with Qualcomm's QCS605 System-on-Chip (SoC), while achieving a real-time operation with more than 10 frames per second.

Factors influencing health behavior practice in patients with coronary artery diseases.

BACKGROUND: The purpose of this study was to investigate the relationships among cardiac rehabilitation knowledge, educational need and health behavior practice in patients with coronary artery disease and explain factors influencing health behavior practice. METHOD: The research participants were 189 patients with coronary artery disease from general hospital located in Korea. Self-evaluation questionnaires were used to collect the data. Data was collected from January to May, 2020. Data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation coefficients and multiple regression with the SPSS 24.0 program. RESULTS: There were significant positive relationships between cardiac rehabilitation knowledge and health behavior practice (r = .37, p < .001), and significant positive relationships between educational need and health behavior practice (r = .17, p = .022). Factors influencing health behavior practice were identified, the most critical predictive factor was age (≥80) (ß = .52), followed by cardiac rehabilitation knowledge (ß = .42), regular exercise (No) (ß = -.25), family history (No) (ß = .24), age (60-69) (ß = .22), cohabitation (No) (ß = -.20) and educational needs (ß = .17). The explanation power of this model was 50% and it was statistically significant (F = 13.42, p < .001). CONCLUSION: This study suggests that cardiac rehabilitation knowledge and educational need should be considered in enhancing cardiac rehabilitation programs designed for patients with coronary artery disease.

Disease experiences of female patients with Hansen's disease residing in settlement in Korea.

PURPOSE: The purpose of this study was to identify female Hansen's disease experience in settlement village in Korea. METHOD: For this study, 11 participants in settlement village were purposively chosen. Data were collected through in-depth individual interviews from July to December 2015. Verbatim transcripts were analyzed following Colaizzi's phenomenological analysis to uncover the meaning of the experiences of the participants. RESULTS: The study results showed that female Hansen's disease experience in settlement village consisted of 9 theme and 4 theme clusters: 1) Inescapable shackles; 2) Suffered as if being in prison,; 3) In no position to be a woman or a mother; 4) Another hometown. CONCLUSION: The findings of this study recommends that health care professionals should pay attention not only to leprosy patients to reduce their physical and psychological suffering but also to the community and public culture to promote integration of Hansenin in the community, continued promotion and reform are needed to overcome the stigma. The results of the present study can help us in a better understanding of various aspects of female patients with Hansen's disease residing in settlement.

Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways.

Landmark-based vehicle localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS). Previously used landmarks in highways such as lane markings lack information on longitudinal positions. To address this problem, lane endpoints can be used as landmarks. This paper proposes two essential components when using lane endpoints as landmarks: lane endpoint detection and its accuracy evaluation. First, it proposes a method to efficiently detect lane endpoints using a monocular forward-looking camera, which is the most widely installed perception sensor. Lane endpoints are detected with a small amount of computation based on the following steps: lane detection, lane endpoint candidate generation, and lane endpoint candidate verification. Second, it proposes a method to reliably measure the position accuracy of the lane endpoints detected from images taken while the camera is moving at high speed. A camera is installed with a mobile mapping system (MMS) in a vehicle, and the position accuracy of the lane endpoints detected by the camera is measured by comparing their positions with ground truths obtained by the MMS. In the experiment, the proposed methods were evaluated and compared with previous methods based on a dataset acquired while driving on 80 km of highway in both daytime and nighttime.

FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization.

In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings.

This paper proposes a method that automatically calibrates four cameras of an around view monitor (AVM) system in a natural driving situation. The proposed method estimates orientation angles of four cameras composing the AVM system, and assumes that their locations and intrinsic parameters are known in advance. This method utilizes lane markings because they exist in almost all on-road situations and appear across images of adjacent cameras. It starts by detecting lane markings from images captured by four cameras of the AVM system in a cost-effective manner. False lane markings are rejected by analyzing the statistical properties of the detected lane markings. Once the correct lane markings are sufficiently gathered, this method first calibrates the front and rear cameras, and then calibrates the left and right cameras with the help of the calibration results of the front and rear cameras. This two-step approach is essential because side cameras cannot be fully calibrated by themselves, due to insufficient lane marking information. After this initial calibration, this method collects corresponding lane markings appearing across images of adjacent cameras and simultaneously refines the initial calibration results of four cameras to obtain seamless AVM images. In the case of a long image sequence, this method conducts the camera calibration multiple times, and then selects the medoid as the final result to reduce computational resources and dependency on a specific place. In the experiment, the proposed method was quantitatively and qualitatively evaluated in various real driving situations and showed promising results.

A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles.

An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.