Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

Dong, Huanan; Wen, Ming; Yang, Zhouwang

doi:10.3390/fi11060123

Cited by 21 publications

(21 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the presented method, future works can treat the task as a regression problem and adopt for example a lightweight multilayer perceptron using as input the aggregated optical flow and depth results from different smaller regions of the original image. Another possibility is the exploitation of the more sophisticated convolutional neural network, which, however would possibly require a larger amount of training data [4].…”

Section: Discussionmentioning

confidence: 99%

“…These methods involve different image processing techniques: background extraction [3,20,23,14], image rectification [3,20,23,12], detecting and tracking reference points [3,20,14] or centroids [23] over successive frames, converting the displacement vectors from the image to the real-world coordinate system. The state-of-the-art results of deep learning in vision tasks makes object detection and tracking [12], locating license plates on vehicles [14], 3D convolutional networks [4] other promising directions in the task of speed estimation. Disadvantages of these approaches and of the traffic enforcement solutions already in use, such as speed or point-to-point cameras, include the need for calibration processes, meticulous positioning of the devices at predefined locations, investment in infrastructure and maintenance.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Intuitive Estimation of Speed using Motion and Monocular Depth Information

Rill

2020

Studia UBB Informatica

View full text Add to dashboard Cite

Advances in deep learning make monocular vision approaches attractive for the autonomous driving domain. This work investigates a method for estimating the speed of the ego-vehicle using state-of-the-art deep neural network based optical flow and single-view depth prediction models. Adopting a straightforward intuitive approach and approximating a single scale factor, several application schemes of the deep networks are evaluated and meaningful conclusions are formulated, such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network results influences speed estimation performance; using the depth and optical flow data from smaller crops of wide images degrades performance. With these observations in mind, a RMSE of less than 1 m/s for ego-speed estimation was achieved on the KITTI benchmark using monocular images as input. Limitations and possible future directions are discussed as well.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Intuitive Estimation of Speed using Motion and Monocular Depth Information

Rill

2020

Studia UBB Informatica

View full text Add to dashboard Cite

show abstract

“…Finally, the use of the Mean Square Error (MSE) and the Root Mean Square Error (RMSE) have been proposed in several works [38, 40, 64, 74 82, 88–92, 98, 100–102 105, 113, 114, 116, 129]. The MSE/RMSE measures the ”distance” between the estimates and the real value of what is being measured, using a sum of square differences between these two values.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed.

show abstract

“…Several studies were conducted to estimate vehicle speed and time spent on the road. These studies include [15], [16], [17], [18], [19], [20], [21], [22], [23], and [24]. These studies can be divided into three.…”

Section: Related Workmentioning

confidence: 99%

Feature Engineering Algorithms for Traffic Dataset

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

View full text Add to dashboard Cite

As a result of an increase in the human population globally, traffic congestion in the urban area is becoming worse, which leads to time-consuming, waste of fuel, and, most importantly, the emission of pollutants. Therefore, there is a need to monitor and estimate traffic density. The emergence of an automatic traffic management system allows us to record and monitor motor vehicles' movement in a road segment. One of the challenges researchers face is when the historical traffic data is given as an annual average that contains incomplete data. The annual average daily traffic (AADT) is an average number of traffic volumes at the roadway segment in a specific location over a year. An example of AADT data is the one given by Road Traffic Volume Malaysia (RTVM), and this data is incomplete. The RTVM provides an average of daily traffic data and one peak hour. The recorded traffic data is for sixteen hours, and the only hourly data given is one hour, from 8.00 am to 9.00 am. Hence there is a need to estimate hourly traffic volume for the remaining hours. Feature engineering can be used to overcome the issue of incomplete data. This paper proposed feature engineering algorithms that can efficiently estimate hourly traffic volume and generate features from the existing dataset for all traffic census stations in Malaysia using queuing theory. The proposed feature engineering algorithms were able to estimate the hourly traffic volume and generate features for three years in Jalan Kepong census station, Kuala Lumpur, Malaysia. The algorithms were evaluated using the Random Forest model and Decision Tree Models. The result shows that our feature engineering algorithms improve machine learning algorithms' performance except for the prediction of N O2 using Random Forest, which shows the highest MAE, MSE, and RMSE when traffic data was included for prediction. The algorithm is applied in one of the traffic census stations in Kuala Lumpur, and it can be used for the other stations in Malaysia. Additionally, the algorithm can also be used for any annual average daily traffic data if it includes average hourly data.

show abstract

Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

Cited by 21 publications

References 29 publications

Intuitive Estimation of Speed using Motion and Monocular Depth Information

Intuitive Estimation of Speed using Motion and Monocular Depth Information

Vision‐based vehicle speed estimation: A survey

Feature Engineering Algorithms for Traffic Dataset

Contact Info

Product

Resources

About