Vehicle speed detection from camera stream using image processing methods

Gerat, Jozef; Sopiak, Dominik; Oravec, Milos; Pavlovičová, Jarmila

doi:10.23919/elmar.2017.8124468

Cited by 28 publications

(30 citation statements)

References 7 publications

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“…If the interest is duplicated, it means that the same interest already exists in the PIT, and therefore, the interest received is discarded (Line 23). If there is any transmission scheduled for the interest stored in the PIT, the transmission will be canceled (Lines [21][22]. This occurs due to a previous transmission of an interest packet by a neighboring vehicle.…”

Section: Overt Vndn Operationsmentioning

confidence: 99%

“…Therefore, VNDN comes up with the prospecting potential to maximize large-scale content delivery, taking into account both the QoS and QoE requirements of applications distributed in Intelligent Transport Systems (ITS) [11][12][13]. Incidentally, ITS are contemplated as a key technology to enable more efficient and secure traffic management that ranges from cooperative and distributed route planning [14][15][16][17], traffic and road alerts [18][19][20], and enhanced video distribution [21][22][23], to advisory systems for driving strategy [24][25][26], and autonomous driving capabilities [27][28][29]. Furthermore, the VNDN concept exploits the ICN communication paradigm by changing the ways of requesting and retrieving content from the network, since the content name is the main element of the network and must be globally unique, persistent, secure, and location-independent [30].…”

Section: Introductionmentioning

confidence: 99%

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Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery

et al. 2020

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The number of connected cars and the massive consumption of digital content on the Internet have increased daily. However, the high mobility of the vehicles, coming from patterns’ variation over time, makes efficient large-scale content distribution quite challenging. In light of this, the emerging Vehicular Named Data Network (VNDN) architecture provides support for content-centric network communications and caching capabilities, which allows reliable and larger-scale content delivery over Vehicular Ad-Hoc Networks (VANETs). This notwithstanding, the high number of interest packets in VNDN tends to introduce broadcast storm occurrences during the cache discovery process. Thus, network performance degradation comes up for the influence of both increased packet loss rates and delays on content recovery during communication between vehicles. This work proposes a new cache discOVEry pRoTocol (OVERT VNDN), which combines the computational geometry and degree centrality concepts to tackle the VNDN performance degradation challenges and issues. The main idea behind OVERT VNDN is to choose the most appropriate relay vehicles to engage interest packets’ delivery within the VNDN, seeking to achieve higher network performance by optimizing broadcast storm incidence. The obtained results suggest that OVERT VNDN outperforms its competitor in the following key performance indicators: (i) improving the cache discovery process by 120.47%; (ii) enhancing the content delivery rate by 43%; and (iii) reducing the number of interest packets by 80.99%.

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Section: Overt Vndn Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery

et al. 2020

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“…Liu and Yamazaki have used a pair of QuickBird panchromatic and multi-spectral images for speed detection [12]. Gerát et al [9] used the combination of Kalman filters and opticalflow approaches to estimate speeds. The former is helpful in avoiding the problem of temporary occlusions, while the latter provides more accurate speed delivery.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Tracking and Speed Estimation from Traffic Videos

Hua

Kapoor

Anastasiu

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer vision approaches to propose an efficient way to predict vehicle speed. In this paper, we introduce some state-of-the-art approaches in vehicle speed estimation, vehicle detection, and object tracking, as well as our solution for Track 1 of the Challenge.

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“…More and more vehicles are being equipped with devices that can provide numerous services and applications for managing street traffic or even the entire traffic system in a large city [2]. Because of that, the Intelligent Transport Systems (ITSs) are envisioned to provide a number of new applications that spread from road and traffic alerts [3][4][5], distributed and cooperative route planning [6][7][8][9][10], several types of video distribution [11][12][13][14][15], driving strategy advisory systems [16][17][18], to autonomous driving capabilities [19][20][21]. A report conducted by the Institute of Economic and Applied Research showed that more than 169,000 traffic accidents were registered on the federal highways in Brazil in 2014 [22].…”

Section: Introductionmentioning

confidence: 99%

ATRIP: Architecture for Traffic Classification Based on Image Processing

et al. 2020

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With the increase of vehicles in large urban centers, there is also an increase in the number of traffic jams and accidents on public roads. The development of a proper Intelligent Transport System (ITS) could help to alleviate these problems by assisting the drivers on route selections to avoid the most congested road sections. Therefore, to improve on this issue, this work proposes an architecture to aid an ITS to detect, analyze, and classify the traffic flow conditions in real time. This architecture also provides a control room dashboard to visualize the information and notify the users about the live traffic conditions. To this end, the proposed solution takes advantage of computer vision concepts to extract the maximum information about the roads to better assess and keep the drivers posted about the traffic conditions on selected highways. The main contribution of the proposed architecture is to perform the detection and classification of the flow of vehicles regardless of the luminosity conditions. In order to evaluate the efficiency of the proposed solution, a testbed was designed. The obtained results show that the accuracy of the traffic classification rate is up to 90% in daylight environments and up to 70% in low light environments when compared with the related literature.

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Vehicle speed detection from camera stream using image processing methods

Cited by 28 publications

References 7 publications

Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery

Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery

Vehicle Tracking and Speed Estimation from Traffic Videos

ATRIP: Architecture for Traffic Classification Based on Image Processing

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