Object detection and tracking (in car cabin)
The rate of fatalities and accidents in Singapore is increasing. Concerningly there has been a spike in the traffic accidents resulting in injuries and fatalities by 2.4% and 26%, respectively just from the year 2022 to 2023. Safety measures needed to be taken to aid in reducing such occurrence...
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Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/177193 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The rate of fatalities and accidents in Singapore is increasing. Concerningly there has been a spike
in the traffic accidents resulting in injuries and fatalities by 2.4% and 26%, respectively just from
the year 2022 to 2023. Safety measures needed to be taken to aid in reducing such occurrence for
the safety of all road users.
One such measure is to utilize technology to come out with safety measures. With the rapid rise in
the computer vision field there are interest to use some of the groundbreaking algorithms and
models to come out with a way to make it safer for road users. To utilize these methods there needs
to be an accurate model established to gather accurate data from within a car cabin.
In light of making accurate detection in a car cabin this report will investigate if adding a tracker
module on top of the existing object detection can improve the overall accuracy in detecting and
forming bounding boxes on the target objects. The report hopes to tackle existing issue of unstable
bounding boxes, false positives detection and wrong data association in different frames through
this method.
With the aid of 2 You Only Look Once (YOLO) models, this report has successfully concluded
that adding a tracker in conjunction with object detection improves the accuracy of tracking objects
across frames. This is evident by the increase in average mAP50-95 values when tracking is used
(0.705 for YOLOv9 and 0.762 for YOLOv8 with tracking) compared to without tracking (0.692
for YOLOv9 and 0.743 for YOLOv8). |
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