publications

2023

1. 

   Baybayin Character Instance Detection

   Adriel Isaiah V. Amoguis, Gian Joseph B. Madrid, Benito Miguel D. Flores IV, and 1 more author

   2023

   arXiv Bib PDF

   @misc{amoguis2023baybayin,
     title = {Baybayin Character Instance Detection},
     author = {Amoguis, Adriel Isaiah V. and Madrid, Gian Joseph B. and IV, Benito Miguel D. Flores and II, Macario O. Cordel},
     year = {2023},
     eprint = {2304.09469},
     archiveprefix = {arXiv},
     primaryclass = {cs.CV},
   }

2. 

   Road Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines

   Adriel Isaiah Valeroso Amoguis, Hernand Ang Hermida, Gian Joseph Bonilla Madrid, and 4 more authors

   In Proceedings of the 2023 6th International Conference on Machine Vision and Applications, 2023

   Abs Bib PDF

   As levels of road traffic congestion increase relative to population density, it is becoming increasingly necessary for traffic managers to have awareness of road situations in real-time to keep up with traffic management. There are already existing techniques and applications in computer vision that traffic managers use to collect real-time telemetry, such as but not limited to vehicle counting algorithms. However, these algorithms and applications may not be lane-aware. Enabling lane awareness to these systems allows them to be more granular, which enables more in-depth telemetry such as lane usage, driver pattern recognition, and anomaly detection, among others. Lane awareness in these systems are enabled by performing lane segmentation. This study investigates two approaches to this. The first approach uses vehicle trajectories to generate aggregated trajectory maps, which are then clustered to determine trajectory lane membership and to generate representative trajectories that describes the lane. On the other hand, the second approach takes an end-to-end method and uses road lane features such as demarcation lines to segment lanes. The first approach proved to be more viable as a lane segmentation algorithm compared to the second approach as it was able to segment lanes more reliably, given enough vehicle trajectories are present.

   @inproceedings{10.1145/3589572.3589582,
     author = {Amoguis, Adriel Isaiah Valeroso and Hermida, Hernand Ang and Madrid, Gian Joseph Bonilla and Marquez, Gabriel Costes and Dy, Justin Opulencia and Guerrero, Jose Gerardo Ortile and Ilao, Joel Paz},
     title = {Road Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines},
     year = {2023},
     isbn = {9781450399531},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3589572.3589582},
     doi = {10.1145/3589572.3589582},
     booktitle = {Proceedings of the 2023 6th International Conference on Machine Vision and Applications},
     pages = {64–71},
     numpages = {8},
     keywords = {traffic engineering, vehicle tracking, transportation engineering, computer vision, road lane segmentation, YOLO},
     location = {Singapore, Singapore},
     series = {ICMVA '23},
   }

3. 

   (Pending Publication) Road Lane Segmentation and Functionality Detection

   Adriel Isaiah Valeroso Amoguis, Gabriel Costes Marquez, Jose Gerardo Ortile Guerrero, and 2 more authors

   2023

   Abs

   Insights derived from surveillance-based road telemetry are vital for traffic engineers, managers, and policymakers to make well-informed decisions regarding traffic policies. However, road telemetry tends to be general for any given road scene, leading to non-granularity and generalization of insights. This can be improved by isolating telemetry based on road lanes through lane segmentation. Knowing and segmenting lanes allows for the analysis of each lane’s individual telemetries which may contribute to various fields such as road vehicle navigation, traffic violation detection, road wear and tear detection for preventive maintenance, and even vision-based road anomaly detection, among others. This paper demonstrates how \textitfunctional lanes are empirically determined given a video recording of road scenes from fixed traffic surveillance cameras, explores its differences from \textitideal lanes derived from lane demarcation lines, and evaluates the fine-grained analysis taken from functional lane telemetry along with comparisons with ideal lanes. As a proof of concept, some example road telemetry is extracted from both types of lanes. Both systems achieved respectable performance. The ideal lane segmentation system achieved a mean pixel-wise mAP of 0.7760 with a mean F1-Score of 0.9789. The functional lane segmentation system achieved a mean silhouette score of 0.4260 with a mean V-Measure of 0.5777. Lastly, proof-of-concept road telemetry was achieved, showing feasibility.