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13 pages, 73945 KiB

Open AccessArticle

Route Positioning System for Campus Shuttle Bus Service Using a Single Camera

by Jhonghyun An

Electronics 2024, 13(11), 2004; https://doi.org/10.3390/electronics13112004 - 21 May 2024

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A route positioning system is a technology that identifies the current route when driving from one stop to the next, commonly found in public transportation systems such as shuttle buses that follow fixed routes. This is especially useful for smaller-scale services, such as [...] Read more.

A route positioning system is a technology that identifies the current route when driving from one stop to the next, commonly found in public transportation systems such as shuttle buses that follow fixed routes. This is especially useful for smaller-scale services, such as shuttle buses, where using expensive technology and sensors for location tracking might not be feasible. Particularly in urban areas with tall buildings or mountainous regions with lots of trees, relying solely on GPS can lead to many errors. Therefore, this paper suggests a cost-effective solution that uses just one camera sensor to accurately determine the location of small-scale transportation services on fixed routes. For this, this paper uses a single-stage detection network that quickly identifies objects and tracks them using a simple algorithm. These detected features are compiled into a “codebook” using the bag-of-visual-words technique. During actual trips, this pre-created codebook is compared with landmarks that the camera sees. This comparison helps to determine the route currently being traveled. To test the effectiveness of this approach, this paper used the route of a shuttle bus on the Gachon University campus, which is similar to a downtown area with tall buildings or a wooded mountainous area. The results showed that the shuttle bus’s route was recognized with an accuracy of 0.60. Areas with distinct features were recognized with an accuracy of 0.99, while stops with simple, nondescript structures were recognized with an accuracy of 0.29. Additionally, applying the SORT algorithm to enhance performance slightly improved the accuracy from 0.60 to 0.61. This demonstrates that our proposed method can effectively perform location recognition using only cameras in small shuttle buses. Full article

(This article belongs to the Special Issue Computer Vision Applications for Autonomous Vehicles)

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17 pages, 8829 KiB

Open AccessArticle

Multiple Moving Vehicles Tracking Algorithm with Attention Mechanism and Motion Model

by Jiajun Gao, Guangjie Han, Hongbo Zhu and Lyuchao Liao

Electronics 2024, 13(1), 242; https://doi.org/10.3390/electronics13010242 - 4 Jan 2024

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Abstract

With the acceleration of urbanization and the increasing demand for travel, current road traffic is experiencing rapid growth and more complex spatio-temporal logic. Vehicle tracking on roads presents several challenges, including complex scenes with frequent foreground–background transitions, fast and nonlinear vehicle movements, and [...] Read more.

With the acceleration of urbanization and the increasing demand for travel, current road traffic is experiencing rapid growth and more complex spatio-temporal logic. Vehicle tracking on roads presents several challenges, including complex scenes with frequent foreground–background transitions, fast and nonlinear vehicle movements, and the presence of numerous unavoidable low-score detection boxes. In this paper, we propose AM-Vehicle-Track, following the proven-effective paradigm of tracking by detection (TBD). At the detection stage, we introduce the lightweight channel block attention mechanism (LCBAM), facilitating the detector to concentrate more on foreground features with limited computational resources. At the tracking stage, we innovatively propose the noise-adaptive extended Kalman filter (NSA-EKF) module to extract vehicles’ motion information while considering the impact of detection confidence on observation noise when dealing with nonlinear motion. Additionally, we borrow the Byte data association method to address unavoidable low-score detection boxes, enabling secondary association to reduce ID switches. We achieve 42.2 MOTA, 51.2 IDF1, and 364 IDs on the test set of VisDrone-MOT with 72 FPS. The experimental results showcase our approach’s highly competitive performance, attaining SOTA tracking performance with a fast speed. Full article

(This article belongs to the Special Issue Computer Vision Applications for Autonomous Vehicles)

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13 pages, 3140 KiB

Open AccessArticle

Prex-Net: Progressive Exploration Network Using Efficient Channel Fusion for Light Field Reconstruction

by Dong-Myung Kim, Young-Suk Yoon, Yuseok Ban and Jae-Won Suh

Electronics 2023, 12(22), 4661; https://doi.org/10.3390/electronics12224661 - 15 Nov 2023

Viewed by 735

Abstract

Light field (LF) reconstruction is a technique for synthesizing views between LF images and various methods have been proposed to obtain high-quality LF reconstructed images. In this paper, we propose a progressive exploration network using efficient channel fusion for light field reconstruction (Prex-Net), [...] Read more.

Light field (LF) reconstruction is a technique for synthesizing views between LF images and various methods have been proposed to obtain high-quality LF reconstructed images. In this paper, we propose a progressive exploration network using efficient channel fusion for light field reconstruction (Prex-Net), which consists of three parts to quickly produce high-quality synthesized LF images. The initial feature extraction module uses 3D convolution to obtain deep correlations between multiple LF input images. In the channel fusion module, the extracted initial feature map passes through successive up- and down-fusion blocks and continuously searches for features required for LF reconstruction. The fusion block collects the pixels of channels by pixel shuffle and applies convolution to the collected pixels to fuse the information existing between channels. Finally, the LF restoration module synthesizes LF images with high angular resolution through simple convolution using the concatenated outputs of down-fusion blocks. The proposed Prex-Net synthesizes views between LF images faster than existing LF restoration methods and shows good results in the PSNR performance of the synthesized image. Full article

(This article belongs to the Special Issue Computer Vision Applications for Autonomous Vehicles)

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