We briefly talked about the difference between detection and recognition. While recognition is concerned with classifying objects (for example, as pedestrians, cars, bicycles, and so on), detection is basically answering the question: is there a pedestrian present in this image?

The core idea behind most detection algorithms is to split up an image into many small patches, and then classify each image patch as either containing a pedestrian or not. This is exactly what we are going to do in this section. In order to arrive at our own pedestrian detection algorithm, we need to perform the following steps:

1. Build a database of images containing pedestrians. These will be our positive data samples.
2. Build a database of images not containing pedestrians. These will be our negative data samples.
3. Train an SVM on the dataset.
4. Apply the SVM to every possible patch of a test image in order to decide whether the overall image contains a pedestrian.