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63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
# Head Detection using Hollywood Heads Dataset
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Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).
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## Task
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Counting number of people on video surveillance camera stream is an important task that will allow us to estimate the number of visitors in a shops, busy hours in a restaurant, etc. To solve this task, we need to be able to detect human heads from different angles. To train object detection model to detect human heads, we can use [Hollywood Heads Dataset](https://www.di.ens.fr/willow/research/headdetection/).
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## The Dataset
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[Hollywood Heads Dataset](https://www.di.ens.fr/willow/research/headdetection/release/HollywoodHeads.zip) contains 369,846 human heads annotated in 224,740 movie frames from Hollywood movies. It is provided in [https://host.robots.ox.ac.uk/pascal/VOC/](PASCAL VOC) format, where for each image there is also an XML description file that looks like this:
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```xml
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<annotation>
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<folder>HollywoodHeads</folder>
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<filename>mov_021_149390.jpeg</filename>
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<source>
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<database>HollywoodHeads 2015 Database</database>
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<annotation>HollywoodHeads 2015</annotation>
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<image>WILLOW</image>
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</source>
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<size>
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<width>608</width>
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<height>320</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>head</name>
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<bndbox>
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<xmin>201</xmin>
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<ymin>1</ymin>
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<xmax>480</xmax>
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<ymax>263</ymax>
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</bndbox>
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<difficult>0</difficult>
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</object>
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<object>
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<name>head</name>
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<bndbox>
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<xmin>3</xmin>
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<ymin>4</ymin>
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<xmax>241</xmax>
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<ymax>285</ymax>
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</bndbox>
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<difficult>0</difficult>
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</object>
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</annotation>
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```
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In this dataset, there is only one class of objects `head`, and for each head, you get the coordinates of the bounding box. You can parse XML using Python libraries, or use [this library](https://pypi.org/project/pascal-voc/) to deal directly with PASCAL VOC format.
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## Training Object Detection
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You can train an object detection model using one of the following ways:
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* Using [Azure Custom Vision](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/object-detection?tabs=visual-studio&WT.mc_id=academic-77998-cacaste) and it's Python API to programmatically train the model in the cloud. Custom vision will not be able to use more than a few hundred images for training the model, so you may need to limit the dataset.
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* Using the example from [Keras tutorial](https://keras.io/examples/vision/retinanet/) to train RetunaNet model.
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* Using [torchvision.models.detection.RetinaNet](https://pytorch.org/vision/stable/_modules/torchvision/models/detection/retinanet.html) build-in module in torchvision.
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## Takeaway
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Object detection is a task that is frequently required in industry. While there are some services that can be used to perform object detection (such as [Azure Custom Vision](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/object-detection?tabs=visual-studio&WT.mc_id=academic-77998-cacaste)), it is important to understand how object detection works and to be able to train your own models. |