OBJECT DETECTION WITHIN IMAGES AND IN VIDEO STREAM BASED
ON INTEREST POINTS
Amin Mohamed Ahsan, Prof. Dr. Dzulkifli B. Mohamad
Faculty of Computer Science and Information Systems,
Universiti Teknologi Malaysia
am2002as@gmail.com, dzulkifli@utm.my
ABSTRACT
In computer vision, object detection is an essential process for many other
applications such as object tracking, object recognition, objects categorization,
event detection, and searching for object in sequence of images or video. It also
has a high relation to other fields such as robots control and medical imaging.
Detecting an object within images or in video stream involves three steps at least
which are features extraction, object classification and object localization; it
depends on researcher’s strategy. In this study and as first step, we present a
method to extract local features based on interest point which is used to detect
key-points within an image, then, computing histogram of gradient (HOG) for the
region surround that point. Proposed method used speed-up robust feature
(SURF) method as interest point detector and exclude its descriptor. The new
descriptor is computed by using HOG method. The proposed method got
advantages of both mentioned methods. To evaluate the proposed method, we
used well-known dataset which is Caltech101. The initial result is encouraging in
spite of using a small data for training; the classifier used to examine features that
are obtained by our method is k-nearest neighborhood (k-NN). Detection rate,
specificity, and precision which are obtained using our method are, 0.85%, 97.8%,
and 90.5% respectively. Currently, we are working on enhancing the classification
and localization methods using a huge data.
KEYWORD
Object Detection, SURF, HOG, k-NN