Precise Vehicle Speed Measurement Based on a Hierarchical Homographic Transform Estimation for Law Enforcement Applications

Eslami, H R; Raie, Abolghasem A.; Faez, Karim

doi:10.1587/transinf.2015edp7371

Cited by 3 publications

(17 citation statements)

References 22 publications

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“…These methods solve the data association problem (associating a unique identifier to each object for each track), but do not filter the state of the tracked object. A particular case of tracking by detection methods is that of license plates, since they allow the use of Optical Character Recognition (OCR) systems as a support for solving the data association problem, making the tracking system more robust [41,49,60,66,68,70,76,77,80,82,94,100,125].…”

Section: Methodologiesmentioning

confidence: 99%

“…Most works use a low-medium focal length (≤ 25mm) since they are designed to cover multiple lanes and a large road stretch. Only a few works implicitly or explicitly highlight the need for a long focal length to increase the accuracy of the speed estimation, even if only one lane is covered by the system [23,31,53,61,68,70,76,77,80,82,107,124].…”

Section: Camera Settingsmentioning

confidence: 99%

“…5.b): obtained with mediumhigh camera resolution, medium focal lengths, medium camera height, covering a medium road segment, multiple lanes and only one direction. Some examples are [18,41,42,45,54,61,76,82,93,108].…”

Section: Camera Settingsmentioning

confidence: 99%

“…Hard calibration to get both camera's intrinsic and extrinsic parameters can be addressed by using standard (e.g., chessboard) or ad-hoc calibration patterns [17,18,37,58,59,68,76,80,100] which are placed on the road plane. The main drawback of this technique is that it requires stopping traffic on the road or at the lane level temporarily.…”

Section: System Calibrationmentioning

confidence: 99%

“…Indeed, it is practically impossible for a detected plate not to correspond to a vehicle, and it is very unlikely that a vehicle will transit without a plate, as this is something that is criminally punishable throughout the world. This makes the license plates a very attractive target for vehicle detection systems [31,41,49,60,66,68,70,76,77,80,82,94,122,125]. Therefore, License Plate Recognition (LPR) systems can be applied when camera resolution is large enough, i.e., from medium to low meter-to-pixel ratios.…”

Section: 13mentioning

confidence: 99%

See 4 more Smart Citations

Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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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.

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Section: Methodologiesmentioning

confidence: 99%

Section: Camera Settingsmentioning

confidence: 99%

Section: Camera Settingsmentioning

confidence: 99%

Section: System Calibrationmentioning

confidence: 99%

Section: 13mentioning

confidence: 99%

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Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Luo,

Bi,

Yang

et al. 2024

Front. Phys.

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Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.

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A Fast and Noise Tolerable Binarization Method for Automatic License Plate Recognition in the Open Environment in Taiwan

et al. 2020

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License plate recognition is widely used in our daily life. Image binarization, which is a process to convert an image to white and black, is an important step of license plate recognition. Among the proposed binarization methods, Otsu method is the most famous and commonly used one in a license plate recognition system since it is the fastest and can reach a comparable recognition accuracy. The main disadvantage of Otsu method is that it is sensitive to luminance effect and noise, and this property is impractical since most vehicle images are captured in an open environment. In this paper, we propose a system to improve the performance of automatic license plates reorganization in the open environment in Taiwan. Our system uses a binarization method which is inspired by the symmetry principles. Experimental results showed that when our method has a similar time complexity to that of Otsu, our method can improve the recognition rate up to 1.30 times better than Otsu.

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Precise Vehicle Speed Measurement Based on a Hierarchical Homographic Transform Estimation for Law Enforcement Applications

Cited by 3 publications

References 22 publications

Vision‐based vehicle speed estimation: A survey

Vision‐based vehicle speed estimation: A survey

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

A Fast and Noise Tolerable Binarization Method for Automatic License Plate Recognition in the Open Environment in Taiwan

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