Enhanced Object Detection in Bird's Eye View Using 3D Global Context Inferred From Lidar Point Data

Kim, Yecheol; Kim, Jaekyum; Koh, Junho; Choi, Jun Won

doi:10.1109/ivs.2019.8814276

Cited by 8 publications

(5 citation statements)

References 29 publications

(29 reference statements)

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“…Many works continue to use sensors (e.g. LiDAR) with deep learning methods such as CNN [23], [24], Generative Adversarial Network (GAN) [25], and Deep Layer Aggregation (DLA) [26] to develop BEV from the frontal view, especially for vehicles and pedestrians. They take advantage of the LiDAR point cloud and the frontal view so as to achieve high-quality and significant performance compared to the baseline detector.…”

Section: B Bird's -Eye View Methodsmentioning

confidence: 99%

An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

et al. 2023

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Vehicle speed estimation is one of the most critical issues in intelligent transportation system (ITS) research, while defining distance and identifying direction have become an inseparable part of vehicle speed estimation. Despite the success of traditional and deep learning approaches in estimating vehicle speed, the high cost of deploying hardware devices to get all related sensor data, such as infrared/ultrasonic devices, Global Positioning Systems (GPS), Light Detection and Ranging (LiDAR systems), and magnetic devices, has become the key barrier to improvement in previous studies. In this paper, our proposed model consists of two main components: 1) a vehicle detection and tracking componentthis module is designed for creating reliable detection and tracking every specific object without doing calibration; 2) homography transformation regression networkthis module has a function to solve occlusion issues and estimate vehicle speed accurately and efficiently. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art methods by reducing the mean square error (MSE) metric from 14.02 to 6.56 based on deep learning approaches. We have announced our test code and model on GitHub with https://github.com/ervinyo/Speed-Estimation-Using-Homography-Transformation-and-Regression-Network.

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Section: B Bird's -Eye View Methodsmentioning

confidence: 99%

An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

et al. 2023

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“…vehicles and pedestrians, typically lie on a surface such as the road or side walk. Given this assumption, bird-eye view(BEV) projections of point-clouds have gained popularity in the field of vehicular object detection [13], [14], [15]. Although BEV projection causes information loss, it has some merits in our particular application.…”

Section: Proposed Feature Sharing Cooperative Object Detection (Fs-cod)mentioning

confidence: 99%

Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks

Marvasti

Raftari

Marvasti

et al. 2020

Preprint

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The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and communication systems are still challenging problems. In this paper, we aim to mitigate the effects of these limitations by introducing the concept of feature sharing for cooperative object detection (FS-COD). In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles while maintaining a balance between computation and communication load. This approach is different from current methods of map sharing, or sharing of raw data which are not scalable. The performance of the proposed approach is verified through experiments on Volony dataset. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.

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“…In addition, it is challenging to recognize pedestrians, which is a priority for detection. The use of the top-view method is presented in Yu et al [9], VeloFCN [10], BirdNet [11], Kim et al [12], Kim et al [13], PIXOR [14], and BirdNet+ [15]. The third method is to transform data into polar-view (or front-view) images.…”

Section: Voxel-based Networkmentioning

confidence: 99%

Channel-Based Network for Fast Object Detection of 3D LiDAR

Kwon

Park

2020

Electronics

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Currently, there are various methods of LiDAR-based object detection networks. In this paper, we propose a channel-based object detection network using LiDAR channel information. The proposed method is a 2D convolution network with data alignment processing stages including a single-step detection stage. The network consists of a channel internal convolution network, channel external convolution network and detection network. First, the convolutional network within the channel divides the LiDAR data for each channel to find features within the channel. Second, the convolutional network outside the channel combines the LiDAR data divided for each channel to find features between the channels. Finally, the detection network finds objects with the features obtained. We evaluate our proposed network using our 16-channel lidar and popular KITTI dataset. We can confirm that the proposed method detects objects quickly while maintaining performance when compared with the existing network.

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Enhanced Object Detection in Bird's Eye View Using 3D Global Context Inferred From Lidar Point Data

Abstract: Recent advancements in Bird's Eye View (BEV) * Corresponding author. † Work done during an internship at NAVER LABS. 1.0m/px 0.5m/px, ×2 Up 0.25m/px, ×4 Up 0.125m/px, ×8 Up

Cited by 8 publications

References 29 publications

An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks

Channel-Based Network for Fast Object Detection of 3D LiDAR

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