The topic of this course is the detection of humans in images.
Object detection is one of the most challenging tasks in the field of computer vision and machine learning. The difficulty is because many objects have complex appearances; for instance, humans often adopt varying poses and have different sizes.
The goal of this project is the detection of object instances in images using local features and supervised learning methods. The students will implement a detector for humans which performs localization by specifying a tight bounding box around each instance.
The project will include the following main tasks
-Extract HOG (Histogram of Oriented Gradients) [2] image features.
-Learn basics about Support Vector Machines and how to use them for classification and regression.
-Evaluation of detection results on images of a pedestrian dataset.
This course is divided into two phases:
-Assignments (handed out every week) will introduce students to programming in OpenCV step-by-step and provide first hands-on experience in image processing and machine learning.
-Student groups will work on a project in weekly sessions.