Car detection at night using latent filters

Tehrani, Hossein; Kawano, Taiki; Mita, Seiichi

doi:10.1109/ivs.2014.6856518

Cited by 16 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With regard to the first step, multilevel thresholding approaches ( 3 , 19 , 20 ), paired vehicle lights ( 21 , 22 ), salient maps ( 6 ), and AdaBoost classifier were included ( 5 ). As for determining the effective ROI of vehicles, Hough transform ( 2 , 23 ), night DPM ( 24 ), and Laplacian of Gaussian operator ( 4 ) and so forth were developed. However, these methods find it difficult to handle complex environmental conditions especially when there are severe light variations.…”

Section: Related Workmentioning

confidence: 99%

Improved Nighttime Traffic Detection Using Day-To-Night Image Transfer

Guo

Jian

Xie

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

With the rapid development of convolutional neural networks (CNNs), real-time traffic monitoring, which benefits both traffic optimization and management, has been widely applied with smart cameras on streets. However, compared with the daytime traffic detection, nighttime traffic detection is limited by labeling annotations and is thus unstable, inaccurate, and inefficient in practice. To address this issue, this study proposes to use the augmented nighttime traffic images generated by cycle generative adversarial networks (CycleGAN) for better nighttime detection performance. Using CycleGAN, transferred nighttime traffic images are generated by using the daytime traffic images, as both share the same annotations of traffic instances in the daytime. For comparison, different learning rates and crop sizes are adopted for day-to-night traffic image transfer. The previously proposed detection network, dense traffic detection network (DTDNet), is adopted to train the prepared image data set. The indicators of mean average precision (mAP), precision, and recall are adopted for training performance evaluation and comparison. Based on the visualization results, CycleGAN, with a learning rate of 2e-5 and a crop size of 64, has better performance on day-to-night traffic image transfer with our proposed image data set. Considering the indicators of training performance, DTDNet, with 60% of transferred nighttime images and 40% of the original images, has better accuracy in four categories. Overall, this study provides a possible solution for addressing the issue of training data limitation in nighttime traffic detection and demonstrates the potential of GAN-based data augmentation in the transportation domain.

show abstract

Section: Related Workmentioning

confidence: 99%

Improved Nighttime Traffic Detection Using Day-To-Night Image Transfer

Guo

Jian

Xie

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…At nighttime, due to low contrast, vehicles are usually detected by locating their headlamps and rear lights singularities in the image space caused by the luminous intensity of the light sources with rule-based algorithms (e. g., López et al, 2008, Alcantarilla et al, 2011, Eum & Jung, 2013, Sevekar & Dhonde, 2016, Pham & Yoo, 2020. However, besides rule-based approaches, methods using NNs (e. g., Oldenziel et al, 2020, Mo et al, 2019, Bell et al, 2021 or different imaging sensors like infrared cameras (e. g., Tehrani et al, 2014, Niknejad et al, 2011 have been investigated as well.…”

Section: Vehicle Detectionmentioning

confidence: 99%

Provident vehicle detection at night for advanced driver assistance systems

Ewecker¹,

Asan

Ohnemus

et al. 2022

Auton Robot

View full text Add to dashboard Cite

In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system’s time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward.

show abstract

“…Other works are focused on detecting not only the lights, but also the entire vehicle. In [28], they detect vehicles using the classifier of Deformable Part Models (DPM) with infrared imagery. In [29], the positions of the vehicles are predicted using Local Binary Patterns (LBP) along with an Adaboost detector.…”

Section: Introductionmentioning

confidence: 99%