• Journal of the Semiconductor & Display Technology
• /
• v.14 no.2
• /
• pp.29-34
• /
• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• Journal of the Korean Society of Safety
• /
• v.24 no.3
• /
• pp.65-70
• /
• 2009

In-vehicle route guidance information(RGI) systems have been developed with the advancement of the information and communication technologies. However, the RGI is provided by a pre-determined option, drivers occasionally pass the target intersection owing to non- or late- recognizing it. The purpose of this experiment is to examine the position of driver's tum signal operation and intersection recognition approaching at the target intersection which is difficult to identify as a preliminary research on developing the additional RGI connecting with the tum signal control. The field experiment was conducted to measure distances of the turn signal operation and intersection recognition from the target intersection according to driving lanes and landmarks at adjacent intersection. And, glance behavior to the car navigation display was evaluated by using an eye camera. The results indicate that drivers operate the turn signal after confirming a landmark in the case of the intersection with it. However, most case of driving, drivers operate the tum signal at 40 to 50m before coming to the target. To provide the additional RGI, when drivers do not operate the tum signal approaching at the target intersection based on the results, is expected to improve the traffic safety and the comfort for drivers.

• Journal of KIBIM
• /
• v.7 no.1
• /
• pp.45-53
• /
• 2017

Continuous research efforts have been made on acquiring location data on construction sites. As a result, GPS and RFID are increasingly employed on the site to track the location of equipment and materials. However, these systems are based on radio frequency technologies which require attaching tags on every target entity. Implementing the systems incurs time and costs for attaching/detaching/managing the tags or sensors. For this reason, efforts are currently being made to track construction entities using only cameras. Vision-based 3D tracking has been presented in a previous research work in which the location of construction manpower, vehicle, and materials were successfully tracked. However, the proposed system is still in its infancy and yet to be implemented on practical applications for two reasons. First, it does not involve entity matching across two views, and thus cannot be used for tracking multiple entities, simultaneously. Second, the use of a checker board in the camera calibration process entails a focus-related problem when the baseline is long and the target entities are located far from the cameras. This paper proposes a vision-based method to track multiple workers simultaneously. An entity matching procedure is added to acquire the matching pairs of the same entities across two views which is necessary for tracking multiple entities. Also, the proposed method simplified the calibration process by avoiding the use of a checkerboard, making it more adequate to the realistic deployment on construction sites.

• Journal of the Semiconductor & Display Technology
• /
• v.17 no.2
• /
• pp.76-80
• /
• 2018

In recent years, with the development of automobile technology, smart system technology that assists safe driving has been developed. A camera is installed on the front and rear of the vehicle as well as on the left and right sides to detect and warn of collision risks and hazards. Beyond the technology of simple black-box recording via cameras, we are developing intelligent systems that combine various computer vision technologies. However, most related studies have been developed to optimize performance in laboratory-like environments that do not take environmental factors such as weather into account. In this paper, we propose a method to detect object by restoring visibility in image with degraded image due to weather factors such as fog. First, the image quality degradation such as fog is detected in a single image, and the image quality is improved by restoring using an intermediate value filter. Then, we used an adaptive feature extraction method that removes unnecessary elements such as noise from the improved image and uses it to recognize objects with only the necessary features. In the proposed method, it is shown that more feature points are extracted than the feature points of the region of interest in the improved image.

• Journal of the Society of Naval Architects of Korea
• /
• v.56 no.4
• /
• pp.327-334
• /
• 2019

Recently, there has been a growing interest in artificial supercavitation as a way to reduce friction drag of submerged vehicles. A cavitator plays an important role to generate the supercavity, so many studies have focused on the case of cavitator only. However, the body shape behind the cavitator affects the growth of the supercavity and this effect must be considered for evaluating the overall performance of the system. In this work, we conducted experimental investigation on artificial supercavitation generated by different combinations of the cavitator and body. We observed the supercavity pattern by using a high-speed camera and measured the pressure inside the cavity by using an absolute pressure transducer. We estimated the relation between the amount of injected air and the supercavity shape for different combinations. In summary, the disk type cavitator generates larger supercavity than that of the cone and ellipsoidal cavitators, but cavity development speed is relatively slower rather than the others. Furthermore, fore body angle plays an important role to generate the supercavity enveloping the entire body.

• Journal of Multimedia Information System
• /
• v.6 no.2
• /
• pp.107-118
• /
• 2019

This paper deals with actual optical data from rural as well as urban areas in a nationwide study captured with Fisheye cameras. Simultaneously data was collected (of the receiving power density) from the mobile communications satellite N-STAR. The visibility of the satellite is easily determined by checking the value of the pixels in the binarized fisheye image of its position. The process of determining the visible satellite is automatically performed. Based on the analyses of the field data measured in Japan, we are expecting HEOs (Highly inclined Elliptical Orbiters) that would reduce blockage in the extreme northern region of Wakkanai City well as in the most crowded urban area, in Tokyo Ginza. In case of HEOs operation, the elevation angle will improve from 37 with N-STAR GEO to 75 degrees. HEOs could replace 5G/Ka-band or support in rural areas where broadband circuit is not available. We are proposing combination operations with HEOs and 5G/Ka-band to solve blockage problems, because HEOs can keep line-of-sight propagation with high elevation angle for long duration. In such operations, the communications profile on the vehicle based on actual optical data will be very useful to predict blockages and to select/switch a suitable circuit.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.5978-5999
• /
• 2018

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.

• The Journal of Korea Robotics Society
• /
• v.19 no.1
• /
• pp.79-91
• /
• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• Journal of Convergence for Information Technology
• /
• v.10 no.5
• /
• pp.111-116
• /
• 2020

Recently, many safety accidents involving children shuttle buses take place. Without a teacher for help, a safety accident occurs when the driver can't see a child who is getting off in the blind spot of both frontside and backside. A deep learning-based smart mirror allows user authentication and provides various services. Especially, It can be a role of helper for children, and prevent accidents that can occur when drivers or assistant teachers do not see them. User authentication is carried out with children's face registered in advance. Safety accidents can be prevented by an approximate sensor and a camera in frontside and backside of the bus. This study suggests a way of checking out whether children are missed in the process of getting in and out of the bus, designs a system that reduce blind spots in the front and back of the vehicle, and builds a safety system that provide various services using GPS.

• Journal of the Ergonomics Society of Korea
• /
• v.28 no.3
• /
• pp.63-71
• /
• 2009

A car navigation system as an in-vehicle route guidance information (RGI) offers a state-of-the-art technological solution to driver navigation in an unfamiliar area. However, the RGI is provided by some pre-determined options in terms of the interface between a driver and a car navigation system. Drivers occasionally pass the target intersection owing to non- or late- recognizing it. This paper is examined the position of driver's turn signal operation and intersection recognition approaching at the target intersection which is difficult to identify, as a fundamental research on developing the additional RGI connecting with the turn signal control. The field experiment was conducted to measure distances of the turn signal operation and the intersection recognition from the target intersection according to left turns, right turns, and landmarks at adjacent intersection. And glance behavior to the car navigation display was evaluated by using an eye camera. The results of the field study indicate that, most case of driving, drivers operate the turn signal until 40m to 50m before coming to the target intersection. The driving simulator experiment was performed to examine the effectiveness of providing the additional RGI when drivers did not operate the turn signal approaching at the target intersection based on the results of the field study. To provide the additional RGI is effective for the intersection identification and recognition, and expected to improve the traffic safety and the comfort for drivers.