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ADRIEL ISAIAH V. AMOGUIS

Research Assistant (ALTDSI) | Graduate & Undergraduate Student
(BSMSCS-Ladderized)

Adriel Isaiah V. Amoguis is a Research Assistant in the Dr. Andrew L. Tan Data
Science Institute (ALTDSI) of De La Salle University, with a passion for
artificial intelligence and machine learning. He is currently completing both a
Bachelor’s and a Master’s degree in Computer Science at De La Salle University -
Manila. Adriel’s primary focus and most important skill is in machine learning
engineering and artificial intelligence, and he has honed these skills through
research and practice. He is a proficient developer in back-end web development,
desktop application development, and database systems management.

Adriel has already made a significant impact in his field, having published two
papers on computer vision, and he is currently working on a project for his
Bachelor’s thesis in the domain of computer vision. He is eager to publish this
work this year and is working hard to get acceptance, presentation, and
publication in the Conference on Computer Vision and Pattern Recognition (CVPR)
for future works.

Adriel is also an avid reader of literature on computer science and technology
and stays up-to-date with the latest advancements in the field through
subscriptions to tech news and by being a member of the Association for
Computing Machinery (ACM).

Adriel’s greatest professional accomplishment thus far is his publication in the
6th International Conference for Machine Vision and Applications (ICMVA 2023).
This SCOPUS-indexed publication led him to the ACM and allowed him to network
with other professionals in his field globally. He is also excited about his
upcoming journal publication on reading ancient Filipino scripts using computer
vision.

Adriel is determined to break through limits and push for something greater in
his career. His mentors have inspired him to aim high, and he is determined to
continue making meaningful contributions to the field of computer science and
artificial intelligence.


NEWS

Oct 19, 2023


ACCEPTED IN M2VIP 2023!

I am pleased to announce our acceptance in M2VIP 2023 taking place in
Queenstown, New Zealand this 21st to 24th of November 2023. The proceedings of
the said conference will be published with IEEE.

I am thrilled and excited to participate in this conference and further the
field of transporation telemetry through the use of computer vision. I shall
release updates as they come.

Sep 11, 2023


I’M INTERNING AT MANULIFE IT DELIVERY CENTER ASIA, INC. UNTIL DECELBER 2023

I’ve been employed as a Software Engineering Intern in Manulife IT Delivery
Center Asia, Inc. (MITDC). I am currently working on Forms Migration, migerating
end-of-life forms in Java Form Builder (FMB) to a newer proprietary software
stack (Java BE, React FE). Aug 1, 2023


UPDATE ON CURRENT PROJECTS

I am pleased to announce the completion and acceptance of my undergraduate
thesis entitled, “Road Lane Segmentation and Functionality Detection”. This is a
super-study of our previously published research article in ICMVA 2023, “Road
Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines”.

We are currently aiming for publication of a conference paper version of our
thesis in the 29th IEEE International Conference on Mechatronics and Machine
Vision In Practice (M2VIP 2023) taking place this 21st to 24th of November,
2023.

Jul 12, 2023


COMPLETION OF UNDERGRADUATE THESIS

I am pleased to announce the completion and acceptance of my undergraduate
thesis entitled, “Road Lane Segmentation and Functionality Detection”. This is a
super-study of our previously published research article in ICMVA 2023, “Road
Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines”.

We are currently aiming for publication of a conference paper version of our
thesis in the 29th IEEE International Conference on Mechatronics and Machine
Vision In Practice (M2VIP 2023) taking place this 21st to 24th of November,
2023.

Jun 10, 2023


BAYBAYIN OCR EXTENSION

Experiments are ongoing now for the extension study of our Baybayin OCR system.
As a publication goal, we have identified PACLIC 2023 as our target conference.
This conference will be in Hong Kong this December.

We are eagerly working on our experiments, and really hope to participate in
this conference. The date to beat for our finished paper is July 16, 2023. Will
give an update by then!

Jun 10, 2023


THE ICMVA 2023 PROCEEDINGS ARE NOW PUBLISHED!

And that’s a wrap! The ICMVA 2023 proceedings are now published in the ACM
Digital Library. You can find my paper here. I’m excited to see my work
published in the SCOPUS database. I’ll be updating my publications page with the
ACM Author-Ized PDF soon, but the publication entry is there should you want to
see it.

I genuinely hope that this work can contribute to the field of traffic systems.
Since this is only a small subset of the work I’m doing for my undergraduate
thesis, I’m excited to see how the rest of my work will contribute to the field.
I’m also hoping that we’re able to publish our finished thesis work either in a
journal, or another conference. May 1, 2023


ASEAN DSE COMPETITION

I’m announcing today my participation in the 2023 ASEAN Data Science Explorers
Competition. I’m participating with my teammate Naomi from Davao Doctors’
College.

The competition is a data analytics competition that aims to empower the youth
of the ASEAN region to contribute to the ASEAN Community’s efforts in addressing
socio-economic issues in the region. The competition is open to all tertiary
students from the 10 ASEAN member states and challenges them to deliver
data-driven insights to solve regional issues. The competition is organized by
the ASEAN Foundation and SAP, in partnership with the ASEAN University Network
(AUN) and supported by the ASEAN Secretariat.

I’m excited to be participating in this competition and I’m looking forward to
the results of our work. I’ll be posting updates on this blog as the competition
progresses. We cannot share much right now as we’re still in the process of
making our SAP Storyboards, but I’ll be sure to share our work here once the
competition is over.

Hoping for the best, and I hope our work can contribute to the ASEAN Community’s
efforts in addressing socio-economic issues in the region. Apr 22, 2023


BAYBAYIN OCR UPDATE

In relation to my previous publication about Baybayin OCR, I’m excited to
announce that me and my colleagues, along with our mentor for this project, Dr.
Macario Cordel II, are gearing for an extension publication on this work. As of
now, we’re still unsure of whether or not we’re going to gear up for a
conference publication or a journal publication, but we’re definitely excited to
share our work with the world – especially in the ASEAN.

I cannot delve much into the details of it yet as experimentation is still
ongoing, but since at least 70% of new content are needed to consider it a new
publication in relation to our previously published conferece paper, you can
definitely look forward to seeing much more improvements and a greater scope of
the project. Mar 12, 2023


PUBLICATION IN ICMVA 2023

I am excited to announce my contribution and participation in the 6th
International Conference for Machine Vision and Applications (ICMVA 2023)! This
is a SCOPUS-indexed conference, and I am honored to be a part of it. I am also
delighted to have been able to network with other professionals in my field
globally through this conference.

I presented a subset of my undergraduate thesis work entitled, “Road Lane
Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines.” The
work is to published in the ICMVA 2023 proceedings, and I am excited to see it
published in the SCOPUS database under the Association for Computing Machinery
(ACM) Digital Library. I’ll definitely be sharing the details in my publications
page once it’s published.

See this link for my feature on the DLSU CCS Facebook page for more details.


--------------------------------------------------------------------------------


SELECTED PUBLICATIONS

 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
    2
    2 Total citations
    2 Recent citations
    n/a Field Citation Ratio
    n/a Relative Citation Ratio
    
    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.

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