arXiv subjects

Manognya Lokesh Reddy

Publications and source records attributed to Manognya Lokesh Reddy.

3 recordsLinked to original sources

arXiv2026

Calibrated Multichannel Monocular Ranging From Standardized License Plates With Metrology-Exact Validation

Longitudinal driver assistance depends on the distance to the vehicle ahead, a quantity normally supplied by radar, laser scanner, or stereo pair. However, a low-cost camera can estimate the distance as well, taking the rear license plate as a metric reference, including standards fix both the plate envelope and the regulated character height, so the pinhole projection converts either one into a distance. This research presents a approach and validates it. Plate localization now withstands the low-contrast and cluttered frames that previously drew the detector onto the vehicle body, or onto the character cluster of a sparsely lettered plate. Character height, plate outline, and mounting-hole span form three distance channels, merged by a consensus gated inverse-variance fusion that discards a corrupted channel before it can bias the result, with each channel separately corrected for the foreshortening of the direction along which it is measured, the correction following from the plate's recovered attitude. Finally, an innovation gate protects the temporal filter, so that a single bad detection cannot become a false warning. Evaluation is carried out in a metrology-exact testbed in which the commanded distance is the true distance, through 51 USA registries with distances of 1 to 12 m and viewing angles to 30 degrees under five lighting conditions. The plate is located in every frame. Mean absolute percentage error is 4.32% for the outline channel, while the fusion returns 5.79% mean and 2.17% median, without any of its single-channel failure modes. Roll is recovered to within one degree; the two out-of-plane tilts only to within several. The algorithm can provide a solid foundation for low-cost distance estimation which can serve as an emergency backup for other sensors.

cs.CV↗

arXiv2026

Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography

Accurate inter-vehicle distance estimation is a cornerstone of Advanced Driver Assistance Systems (ADAS) and autonomous driving. While LiDAR and radar provide high precision, their high cost prohibits widespread adoption in mass-market vehicles. Monocular camera-based estimation offers a low-cost alternative but suffers from fundamental scale ambiguity. Recent deep learning methods for monocular depth achieve impressive results yet require expensive supervised training, suffer from domain shift, and produce predictions that are difficult to certify for safety-critical deployment. This paper presents a framework that exploits the standardized typography of United States license plates as passive fiducial markers for metric ranging, resolving scale ambiguity through explicit geometric priors without any training data or active illumination. First, a four-method parallel plate detector achieves robust plate reading across the full automotive lighting range. Second, a three-stage state identification engine fusing optical character recognition text matching, multi-design color scoring, and a lightweight neural network classifier provides robust identification across all ambient conditions. Third, hybrid depth fusion with inverse-variance weighting and online scale alignment, combined with a one-dimensional constant-velocity Kalman filter, delivers smoothed distance, relative velocity, and time-to-collision for collision warning. Baseline validation on a controlled static dataset reproduces a 2.3% coefficient of variation in character height measurements and a 36% reduction in distance-estimate variance compared with plate-width methods from prior work.

cs.CV↗

arXiv2026

Typography-Based Monocular Distance Estimation Framework for Vehicle Safety Systems

Accurate inter-vehicle distance estimation is a cornerstone of advanced driver assistance systems and autonomous driving. While LiDAR and radar provide high precision, their cost prohibits widespread adoption in mass-market vehicles. Monocular vision offers a low-cost alternative but suffers from scale ambiguity and sensitivity to environmental disturbances. This paper introduces a typography-based monocular distance estimation framework, which exploits the standardized typography of license plates as passive fiducial markers for metric distance estimation. The core geometric module uses robust plate detection and character segmentation to measure character height and computes distance via the pinhole camera model. The system incorporates interactive calibration, adaptive detection with strict and permissive modes, and multi-method character segmentation leveraging both adaptive and global thresholding. To enhance robustness, the framework further includes camera pose compensation using lane-based horizon estimation, hybrid deep-learning fusion, temporal Kalman filtering for velocity estimation, and multi-feature fusion that exploits additional typographic cues such as stroke width, character spacing, and plate border thickness. Experimental validation with a calibrated monocular camera in a controlled indoor setup achieved a coefficient of variation of 2.3% in character height across consecutive frames and a mean absolute error of 7.7%. The framework operates without GPU acceleration, demonstrating real-time feasibility. A comprehensive comparison with a plate-width based method shows that character-based ranging reduces the standard deviation of estimates by 35%, translating to smoother, more consistent distance readings in practice, where erratic estimates could trigger unnecessary braking or acceleration.

cs.CV↗