Head Detection using Hollywood Heads Dataset

Lab Assignment from AI for Beginners Curriculum.

Task

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.

The Dataset

Hollywood Heads Dataset 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/ format, where for each image there is also an XML description file that looks like this:

<annotation>
    <folder>HollywoodHeads</folder>
    <filename>mov_021_149390.jpeg</filename>
    <source>
        <database>HollywoodHeads 2015 Database</database>
        <annotation>HollywoodHeads 2015</annotation>
        <image>WILLOW</image>
    </source>
    <size>
        <width>608</width>
        <height>320</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>head</name>
        <bndbox>
            <xmin>201</xmin>
            <ymin>1</ymin>
            <xmax>480</xmax>
            <ymax>263</ymax>
        </bndbox>
        <difficult>0</difficult>
    </object>
    <object>
        <name>head</name>
        <bndbox>
            <xmin>3</xmin>
            <ymin>4</ymin>
            <xmax>241</xmax>
            <ymax>285</ymax>
        </bndbox>
        <difficult>0</difficult>
    </object>
</annotation>

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 to deal directly with PASCAL VOC format.

Training Object Detection

You can train an object detection model using one of the following ways:

Using Azure Custom Vision 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.
Using the example from Keras tutorial to train RetunaNet model.
Using torchvision.models.detection.RetinaNet build-in module in torchvision.

Takeaway

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), it is important to understand how object detection works and to be able to train your own models.