Rear-Lamp Localization with Real-Coded Genetic Algorithm at Nighttime

Nakane, Takumi; Sun, Haitian; Zhang, Chao

doi:10.1109/gcce50665.2020.9291968

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…The rear-lamp detection method proposed by Jeong et al [8] was a tone-mapping process. Nakane et al [11] detected rear-lamp by genetic algorithm (GA) that was powered by probabilistic bit-wise operation (PBO) with the thresholds of [9] and the strategy of [7]. Parvin et al [19] proposed the detection of rear lamps by red color, but their experiments had a simple dark background including only one vehicle.…”

Section: Related Workmentioning

confidence: 99%

“…However, the candidates are easily misled by other light sources (e.g., neon lamps) because the color feature is naive. Moreover, sampling candidates from the entire image or a relatively large region of interest (ROI) [11] increases not only the computational cost but also the risk of tracking failure. In reality, the position and appearance of the target rear lamps in two adjacent frames are almost consistent [12].…”

Section: Introductionmentioning

confidence: 99%

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Deep Hierarchical Rear‐Lamp Tracking at Nighttime

Sun,

Chen,

Zhang

et al. 2023

IEEJ Transactions Elec Engng

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Rear‐lamp tracking at night is a research topic in night vision that is essential for Advanced Driver Assistance Systems (ADAS). Most current computer vision‐based methods address this problem using color features because rear lamps are lit during night driving. However, such methods are sufficiently robust in complex environments owing to the lack of feature diversity. On the other hand, as there is no off‐the‐shelf dataset, the application of deep learning to nighttime rear‐lamp tracking has been sparsely explored. To tackle the above issues, in this paper, we propose hierarchical rear‐lamp tracking at nighttime (H‐RTN) and create a novel dataset for rear‐lamp tracking. Specifically, the H‐RTN consists of a rough detection hierarchy (R‐hierarchy), an accurate detection hierarchy (A‐hierarchy), and an optimization hierarchy (O‐hierarchy). The R‐hierarchy determines the region of interest (ROI) containing the target rear lamps, the A‐hierarchy samples the rear‐lamp candidates in the ROI, and the O‐hierarchy selects the best pair of candidates as the final location of the target rear lamps. The experimental results show that H‐RTN outperforms the alternative existing methods. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Hierarchical Rear‐Lamp Tracking at Nighttime

Sun,

Chen,

Zhang

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

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“…Jeong et al [11] proposed a tone-mapping process to locate rear-lamps. Nakane et al [13] combined PBO with the thresholds in [12] and the strategy in [10]. The method of [13] is our previous work, which aims at detecting the rear-lamps from a single image, rather than videos.…”

Section: A Rear-lamp Detection At Nighttimementioning

confidence: 99%

“…The second term f 2 considers color information, in which adaptive thresholds are proposed. Casares et al [10] and Nakane et al [13] utilized the fixed thresholds to extract red and white regions, while the extraction result of rear-lamp regions by color can change due to different illuminations. To solve this problem, based on rear-lamp images, we statistically cluster the red pixels in rear-lamp regions into three clusters, as shown in Table 1.…”

Section: ) Optimizationmentioning

confidence: 99%

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Evolutionary Rear-Lamp Tracking at Nighttime

et al. 2021

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Rear-lamp tracking at nighttime plays a momentous role in the advanced driver assistance system (ADAS), involving collision mitigation, automatic cruise control, automatic headlamp dimming, etc. Most of the existing tracking methods based on monocular camera leverage on color features. However, such tracking methods can be easily influenced by background clutter, illumination change, distance variation, and occlusion. In this paper, we propose an evolutionary adaptive rear-lamp tracking method at nighttime, in which a novel genetic algorithm powered by the probabilistic bitwise operation (PBO) is utilized. Also, to improve the robustness against various environments, a balanced fitness function is proposed by taking color information, symmetry, spatial relationship, and rigidity into account. Especially, a series of adaptive thresholds based on rear data in HSV color space is proposed to exploit color information reasonably with respect to our task. A strategy to deal with occlusion is also proposed, which relies on color information and rigidity. Moreover, to our knowledge, there is no publicly available dataset for rear-lamp tracking at nighttime. To fill the gap between the real-world application and the theoretical research, we create a novel dataset, which contains diverse traffic conditions at nighttime. The experimental results indicate that our method outperforms comparative online tracking methods in terms of success rate and center location error.

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