OceanCV
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Southern_Sea_Otter_Tracking

Object Detection

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otters

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sophia-m commited on Mar 13, 2025

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@@ -13,7 +13,7 @@ tags:
 **By Sophia Mangrubang**
 ---
-## Scientific Context
 Behavioral observation and data collection is an integral part of maintaining animals in captivity. Large corporations, such as zoos, rely on staff members to observe animal behaviors as a means of assessing their health. However, without constant surveillance, key behavioral patterns can be overlooked. Machine learning provides a unique opportunity to reduce the time-commitment of staff, increase monitoring time, and produce visualizations that provide valuable insights.
 Currently, there are a few major products that utilize machine learning methods for behavioral observation in captivity. These models are insightful, but lack the accessibility and customization of an open source model.
@@ -22,7 +22,7 @@ My dataset, consisting of Southern Sea Otter enclosure live feeds, will serve as
 ---
-## Dataset Description
 My dataset consists of 3 live feed videos of Southern Sea Otter enclosures. One video is from the Georgia Aquarium, and two are from the Monterey Bay Aquarium. The dataset consists of 348 raw images, and around 900 after the augmentations were added. Within all 3 videos, Southern Sea Otters are the only class present and there are 3 - 5 in each enclosure.
@@ -46,7 +46,7 @@ Videos 2 & 3:
 - **Citation:** Sea Otter Cam. Montereybayaquarium.org. (2025). https://www.montereybayaquarium.org/animals/live-cams/sea-otter-cam
 ---
-## Model Selection
 ### YOLOv11 Object Detection Model
 I chose to use YOLOv11 to create a custom object detection model. The model was trained to detect the presence of Southern Sea Otters in water and on land. I chose an object detection model because it would allow me to add additional features to my model through supplementary code snippets. The features would produce additional visualizations to better interpret movement patterns and behaviors from the dataset. I manipulated the code [found in our textbook](https://oceancv.org/) to better identify and correctly label the otters in lower resolution cam footage, as well as in both land and water.
@@ -57,7 +57,7 @@ I also utilized Ultralytics’ heatmap code to display a heatmap on the input vi
 ---
-## Model Assessment
 ### Here are the metrics I used to assess the accuracy and performance of my model during training.
 ### Confusion Matrix
@@ -74,7 +74,7 @@ https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/blob/main/F1_curve.pn
 My final object detection output video was a key metric in assessing the performance of my model. I bounced between looking at the output video, assessing how accurate the bounding boxes and identifications were, and rerunning the model with modified parameters. My final model output was successful at identifying sea otters in both land and water, with minimal misclassifications or missed detections. The final video can be found on my [repository](https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/resolve/main/object_detection_final.avi?download=true)
 ---
-## Model Use-case
 **Example Proposal:** Utilize the Behavioral Tracking Model to track and analyze the movement patterns of Sea Otters between water, land, and secluded zones to assess their interactions with their enclosure.
 **Example Hypothesis:** Southern Sea Otters will congregate in the water zone most frequently and for the most time, and will rarely enter the seclusion zone.
@@ -84,7 +84,7 @@ My final object detection output video was a key metric in assessing the perform
 **Model Justification:** My model would be a reasonable tool for initial data collection, providing species interaction data across zones, animal frequency, and areas of interest within the enclosure.
 ---
-## Disclaimer
 Credit for all videos used in the dataset go to the [Monterey Bay Aquarium](https://www.montereybayaquarium.org/) and [Georgia Aquarium](https://www.georgiaaquarium.org/).
 - **Video 1:** [Link to Youtube video used](https://www.youtube.com/watch?v=bfjxsHrgeQs)
 - **Video 2 & 3:** [Link to Youtube video used](https://www.youtube.com/watch?v=9JlkEzxk6Qc)

 **By Sophia Mangrubang**
 ---
+# Scientific Context
 Behavioral observation and data collection is an integral part of maintaining animals in captivity. Large corporations, such as zoos, rely on staff members to observe animal behaviors as a means of assessing their health. However, without constant surveillance, key behavioral patterns can be overlooked. Machine learning provides a unique opportunity to reduce the time-commitment of staff, increase monitoring time, and produce visualizations that provide valuable insights.
 Currently, there are a few major products that utilize machine learning methods for behavioral observation in captivity. These models are insightful, but lack the accessibility and customization of an open source model.
 ---
+# Dataset Description
 My dataset consists of 3 live feed videos of Southern Sea Otter enclosures. One video is from the Georgia Aquarium, and two are from the Monterey Bay Aquarium. The dataset consists of 348 raw images, and around 900 after the augmentations were added. Within all 3 videos, Southern Sea Otters are the only class present and there are 3 - 5 in each enclosure.
 - **Citation:** Sea Otter Cam. Montereybayaquarium.org. (2025). https://www.montereybayaquarium.org/animals/live-cams/sea-otter-cam
 ---
+# Model Selection
 ### YOLOv11 Object Detection Model
 I chose to use YOLOv11 to create a custom object detection model. The model was trained to detect the presence of Southern Sea Otters in water and on land. I chose an object detection model because it would allow me to add additional features to my model through supplementary code snippets. The features would produce additional visualizations to better interpret movement patterns and behaviors from the dataset. I manipulated the code [found in our textbook](https://oceancv.org/) to better identify and correctly label the otters in lower resolution cam footage, as well as in both land and water.
 ---
+# Model Assessment
 ### Here are the metrics I used to assess the accuracy and performance of my model during training.
 ### Confusion Matrix
 My final object detection output video was a key metric in assessing the performance of my model. I bounced between looking at the output video, assessing how accurate the bounding boxes and identifications were, and rerunning the model with modified parameters. My final model output was successful at identifying sea otters in both land and water, with minimal misclassifications or missed detections. The final video can be found on my [repository](https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/resolve/main/object_detection_final.avi?download=true)
 ---
+# Model Use-case
 **Example Proposal:** Utilize the Behavioral Tracking Model to track and analyze the movement patterns of Sea Otters between water, land, and secluded zones to assess their interactions with their enclosure.
 **Example Hypothesis:** Southern Sea Otters will congregate in the water zone most frequently and for the most time, and will rarely enter the seclusion zone.
 **Model Justification:** My model would be a reasonable tool for initial data collection, providing species interaction data across zones, animal frequency, and areas of interest within the enclosure.
 ---
+# Disclaimer
 Credit for all videos used in the dataset go to the [Monterey Bay Aquarium](https://www.montereybayaquarium.org/) and [Georgia Aquarium](https://www.georgiaaquarium.org/).
 - **Video 1:** [Link to Youtube video used](https://www.youtube.com/watch?v=bfjxsHrgeQs)
 - **Video 2 & 3:** [Link to Youtube video used](https://www.youtube.com/watch?v=9JlkEzxk6Qc)