Machine learning using template matching applied to object tracking in video data

Zuehlke, David; Henderson, Troy; McMullen, Sonya A. H.

doi:10.1117/12.2518982

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Human error causes thousands of innocent deaths, including pedestrians and other drivers during driving and especially during lane changing. To solve this problem, computer vision techniques like hough transformation [16], edge detection [17], template matching [18] are being implemented based on low-level features including color, texture, and so on. But none of these techniques is perfect due to the constraints of light, shades, clouds, weather, and environmental changes [19].…”

Section: Related Workmentioning

confidence: 99%

Efficient lane marking detection using deep learning technique with differential and cross-entropy loss

Mamun

Ping

Hossen

et al. 2022

IJECE

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<p>Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learning-based model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.</p>

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Section: Related Workmentioning

confidence: 99%

Efficient lane marking detection using deep learning technique with differential and cross-entropy loss

Mamun

Ping

Hossen

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…At the time of changing the road lanes, it causes a thousand of innocent people's death due to human errors. A plethora of image processing techniques can be employed, such as Hough transformation [17], template matching [18], edge detection [19] to detect the lanes in which low-level features, texture and color features had been employed. But these conventional techniques are not appropriate for lane marking detection due to distinct appearance, position, place, the intensity of light, and barricading vehicles, which occlude lane marking [20].…”

Section: Related Workmentioning

confidence: 99%

Lane marking detection using simple encode decode deep learning technique: SegNet

Mamun

Ping

Hossen

2021

IJECE

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<span>In recent times, many innocent people are suffering from sudden death for the sake of unwanted road accidents, which also riveting a lot of financial properties. The researchers have deployed advanced driver assistance systems (ADAS) in which a large number of automated features have been incorporated in the modern vehicles to overcome human mortality as well as financial loss, and lane markings detection is one of them. Many computer vision techniques and intricate image processing approaches have been used for detecting the lane markings by utilizing the handcrafted with highly specialized features. However, the systems have become more challenging due to the computational complexity, overfitting, less accuracy, and incapability to cope up with the intricate environmental conditions. Therefore, this research paper proposed a simple encode-decode deep learning model to detect lane markings under the distinct environmental condition with lower computational complexity. The model is based on SegNet architecture for improving the performance of the existing researches, which is trained by the lane marking dataset containing different complex environment conditions like rain, cloud, low light, curve roads. The model has successfully achieved 96.38% accuracy, 0.0311 false positive, 0.0201 false negative, 0.960 F1 score with a loss of only 1.45%, less overfitting and 428 ms per step that outstripped some of the existing researches. It is expected that this research will bring a significant contribution to the field lane marking detection.</span>

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Fast template matching in multi-modal image under pixel distribution mapping

Mei¹,

Wang²,

Wang³

et al. 2022

Infrared Physics & Technology

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Machine learning using template matching applied to object tracking in video data

Cited by 5 publications

References 2 publications

Efficient lane marking detection using deep learning technique with differential and cross-entropy loss

Efficient lane marking detection using deep learning technique with differential and cross-entropy loss

Lane marking detection using simple encode decode deep learning technique: SegNet

Fast template matching in multi-modal image under pixel distribution mapping

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