Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Apeltauer, Jiří; Babinec, Adam; Herman, David; Apeltauer, Tomáš

doi:10.5194/isprsarchives-xl-3-w2-9-2015

Cited by 49 publications

(24 citation statements)

References 29 publications

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“…It lowers the precision of the trajectory extraction. J. Apeltauer.et.al proposed a method via Boosted classifier and sequential particle filter to detect and track [21]. And background subtraction in aerial videos is adopted in vehicle detection by Azevedo et al They correlate these position result by k-shortest disjoints paths algorithm [22].…”

Section: Related Workmentioning

confidence: 99%

Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

Feng

Fan

et al. 2020

IEEE Access

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Vehicle trajectory data under mixed traffic conditions provides critical information for urban traffic flow modeling and analysis. Recently, the application of unmanned aerial vehicles (UAV) creates a potential of reducing traffic video collection cost and enhances flexibility at the spatial-temporal coverage, supporting trajectory extraction in diverse environments. However, accurate vehicle detection is a challenge due to facts such as small vehicle size and inconspicuous object features in UAV videos. In addition, camera motion in UAV videos hardens the trajectory construction procedure. This research aims at proposing a novel framework for accurate vehicle trajectory construction from UAV videos under mixed traffic conditions. Firstly, a Convolution Neural Network (CNN)-based detection algorithm, named You Only Look Once (YOLO) v3, is applied to detect vehicles globally. Then an image registration method based on Shi-Tomasi corner detection is applied for camera motion compensation. Trajectory construction methods are proposed to obtain accurate vehicle trajectories based on data correlation and trajectory compensation. At last, the ensemble empirical mode decomposition (EEMD) is applied for trajectory data denoising. Our framework is tested on three aerial videos taken by an UAV on urban roads with one including intersection. The extracted vehicle trajectories are compared with manual counts. The results show that the proposed framework achieves an average Recall of 91.91% for motor vehicles, 81.98% for non-motorized vehicles and 78.13% for pedestrians in three videos.

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Section: Related Workmentioning

confidence: 99%

Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

Feng

Fan

et al. 2020

IEEE Access

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“…Similarly, Shirazi and Morris [51] used a Gaussian mixture model to detect vehicles at signalized intersections together with a Kalman filter for trajectory extraction. Apeltauer et al [12] developed another automated method for trajectory extraction in which vehicles are detected using a two-stage classifier trained based on multi-block local binary pattern (MB-LBP) features. This method also requires applying background subtraction in order to generate the foreground mask.…”

Section: Automatic Trajectory Extractionmentioning

confidence: 99%

“…This framework involved video preprocessing and stabilization, vehicle detection and tracking, and ultimately, management of the extracted trajectories. Similar to [11,12], Khan et al [13] carried out the vehicles' detection using background subtraction algorithm.…”

Section: Automatic Trajectory Extractionmentioning

confidence: 99%

“…This becomes particularly problematic when a large number of trajectories are required for building an accurate vehicle trajectory model. Alternatively, automatic trajectory extraction techniques have been proposed [11][12][13]. Yet, most previously developed automatic trajectory extraction approaches require background subtraction to detect moving vehicles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

2020

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This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

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“…As stated earlier, the semi-automatic approach requires a great deal of time for processing while, on the other hand, the automatic approach promises a quick processing and analysis procedure, ultimately leading to the real-time analysis of the UAV acquired data. Recently, the number of studies based on the automated approach have increased [1,[15][16][17][18]. The authors have been attempting to extract various traffic parameters and vehicle trajectories in an automatic environment by using state-of-the art object detection and tracking algorithms.…”

Section: Related Workmentioning

confidence: 99%

Unmanned Aerial Vehicle-Based Traffic Analysis: A Case Study for Shockwave Identification and Flow Parameters Estimation at Signalized Intersections

et al. 2018

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Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles (UAVs), or drones, have become increasingly popular. However, the civil applications of this technology, particularly for traffic data collection and analysis, still need to be thoroughly explored. For this purpose, the authors previously proposed a detailed methodological framework for the automated UAV video processing in order to extract multi-vehicle trajectories at a particular road segment. In this paper, however, the main emphasis is on the comprehensive analysis of vehicle trajectories extracted via a UAV-based video processing framework. An analytical methodology is presented for: (i) the automatic identification of flow states and shockwaves based on processed UAV trajectories, and (ii) the subsequent extraction of various traffic parameters and performance indicators in order to study flow conditions at a signalized intersection. The experimental data to analyze traffic flow conditions was obtained in the city of Sint-Truiden, Belgium. The generation of simplified trajectories, shockwaves, and fundamental diagrams help in analyzing the interrupted-flow conditions at a signalized four-legged intersection using UAV-acquired data. The analysis conducted on such data may serve as a benchmark for the actual traffic-specific applications of the UAV-acquired data. The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. The future research will mainly focus on further extensions of UAV-based traffic applications.

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Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Cited by 49 publications

References 29 publications

Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

Unmanned Aerial Vehicle-Based Traffic Analysis: A Case Study for Shockwave Identification and Flow Parameters Estimation at Signalized Intersections

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