• ETRI Journal
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• v.45 no.5
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• pp.836-846
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• 2023

Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian-rich spaces. This study proposes real-time, accurate, three-dimensional (3D) multi-pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected-component algorithm. The multi-pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.1
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• pp.66-71
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• 2016

According to the development of depth sensing devices and depth estimation technology, depth information becomes more important for object detection in computer vision. In terms of recognition rate, pedestrian detection methods have been improved more accurately. However, the methods makes slower detection time. So, many researches have overcome this problem by using GPU. Here, we propose a real-time pedestrian detection algorithm that does not rely on GPU. First, the depth-weighted distance map is used for detecting expected human regions. Next, human detection is performed on the regions. The performance for the proposed approach is evaluated and compared with the previous methods. We show that proposed method can detect human about 7 times faster than conventional ones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.117-123
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• 2023

There are casualties due to inundation and flooding due to intensive typhoons or heavy rains in summer. Due to such damage, the biggest disaster is flood, and in order to reduce human damage, this paper proposes a shortest distance algorithm-based pedestrian path search study using Map API. This system selects Map API through comparative analysis and provides the shortest route. The route explored is in JSON format and the data of the shelter is stored in the database. The route search system designed and implemented based on this data locates pedestrians and provides evacuation routes in case of flash floods. In addition, if the route cannot be entered while moving to the evacuation route, the current location of the pedestrian is identified, the route is re-searched, and a new route is provided. Therefore, it is believed that the pedestrian route search system proposed in this paper will prevent negligent accidents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.617-624
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• 2015

The conflict of opposing pedestrian traffic-flow in a subway station(made up of stair, passageway, and escalator) diminishes the convenience and mobility of its users. In addition, the station's efficiency would be negatively affected by the growth of delay and queue length in pedestrian facilities. As these phenomena have been resulted by the overlapping in pedestrian's traffic-line, the separation of it would alleviate these problems. For the criteria and methodology of separation, this paper has investigated the bi-directional queue length and delay on the entrance of each facility (stair, passageway and escalator). Since the pedestrian flow exists bidirectionally, we have used the weighted average by inflow rate for the delay value. For the optimization of the separation, the Genetic Algorithm has been utilized in order to minimize the delay.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.111-116
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• 2021

This study is a study on improving the pedestrian Care system, which delivers jaywalking events in real time to the autonomous driving control center and Autonomous driving vehicles in operation and issues warnings and announcements to pedestrians based on pedestrian signals. In order to secure reliability of object detection method of pedestrian Care system, the inspection method combined with camera sensor with Lidar sensor and the improved system algorithm were presented. In addition, for the occurrence events of Lidar sensors and intelligent CCTV received during the operation of autonomous driving vehicles, the system algorithm for the elimination of overlapping events and the improvement of accuracy of the same time, place, and object was presented.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.67-79
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• 2017

Increasing transportation dependence on the metro system has lead to the convenience of passengers becoming as important as the transportation capacity. In this study, a pedestrian simulator has been developed that can quantitatively assess the pedestrian environment in terms of attributes such as speed and distance. The simulator consists of modules designed for 3D indoor map authoring and algorithmic pedestrian modeling. Module functions for 3D indoor map authoring include 3D spatial modeling, network generation, and evaluation of obtained results. The pedestrian modeling algorithm executes functions such as conducting a path search, allocation of users, and evaluation of level of service (LOS). The primary objective behind developing the said functions is to apply and analyze various scenarios repeatedly, such as before and after the improvement of the pedestrian environment, and to integrate the spatial information database with the dynamic information database. Furthermore, to demonstrate the practical applicability of the proposed simulator in the future, a test-bed was constructed for a currently operational metro station and the quantitative index of the proposed improvement effect was calculated by analyzing the walking speed of pedestrians before and after the improvement of the passage. The possibility of database extension for further analysis has also been discussed in this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.609-617
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• 2016

In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.

• Journal of IKEEE
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• v.18 no.1
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• pp.106-113
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• 2014

A pedestrian detector can be applied for various purposes such as monitoring or counting the number of people in some place, or detecting the people plunging in the driveway. There was a lot of related research. But, the detection speed is slow in embedded system because of the limited computing power. An algorithm for fast pedestrian detector using HOG in ARM SIMD architecture is presented in this paper. There is a way to quickly remove the background of image and to improve the detection speed using NEON parallel technique. When we tested with INRIA Person Dataset, the proposed pedestrian detector improves the speed by 3.01 times than previous one.