Vehicle detection in intelligent transportation systems and its applications under varying environments: A review

Yang, Zi; Pun-Cheng, Lilian S.C.

doi:10.1016/j.imavis.2017.09.008

Cited by 199 publications

(103 citation statements)

References 68 publications

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“…There are many competition benchmarks, i.e., PASCAL-VOC [5,6], ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [2], MS-COCO [7], and VisDrone-DET [8]. There are several notable studies on specific object detection like face detection [9], pedestrian detection [10] and vehicle detection [11]. Recently, the research community has focused on deep learning and its applications towards the object recognition/detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

et al. 2020

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Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images.

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Section: Introductionmentioning

confidence: 99%

Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

et al. 2020

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“…One of the main problems remains the determination of the speed, position and other parameters of such moving objects [11][12][13][14]. For solving such problems, sensor systems based on radar technology are currently widely used [15,16]. In this regard, the urgent task is to detect and filter false targets when using millimeter-wave radars [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Millimeter Wave Radar for Intelligent Transportation Systems: a Case Study of Multi-Target Problem Solution

et al. 2020

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The influence of millimeter-wave radar receiver noise on the probability of unambiguous determination of unmanned vehicles speed and range in the intelligent transportation system of the «smart city» is investigated. For the proposed new multi-target detection method for FMCW radar, the effect of the technical parameters of the vehicle radars on the required signal-to-noise ratio (SNR) of the receiver is estimated to ensure the probability of true determination of target parameters at 98%.

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“…By detecting the vehicle on the input image using image processing, various information about the detected vehicle can be obtained. Vehicle detection research includes, for example, feature-based template matching methods [18][19][20][21][22], neural networks or support vector machines [23][24][25], or shape-and motion-based methods [19,26,27]. Vehicle detection methods are mostly based on features that assume an invariable and formal shape of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Kim

2019

Symmetry

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In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduce the throughput and speed-up the processing. To do this, the proposed method decomposed the input image into multiple-resolution images for real-time processing and then extracted the aggregated channel features (ACFs). The idea was to extract only the most important features from images at different resolutions symmetrically. A method of detecting an object and a method of estimating a vehicle’s distance from a bird’s eye view through inverse perspective mapping (IPM) were applied. In the proposed method, ACFs were used to generate the AdaBoost-based vehicle detector. The ACFs were extracted from the LUV color, edge gradient, and orientation (histograms of oriented gradients) of the input image. Subsequently, by applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected. The proposed method was applied in a real-world road environment and showed accurate results for vehicle detection and distance estimation in real-time processing. Thus, it was showed that our method is applicable to autonomous vehicles.

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Vehicle detection in intelligent transportation systems and its applications under varying environments: A review

Cited by 199 publications

References 68 publications

Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

Millimeter Wave Radar for Intelligent Transportation Systems: a Case Study of Multi-Target Problem Solution

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

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