Detecting Multiple Objects Using Blob Detection
Compared to still images, video sequences provide more information about how objects and scenarios change over time. For many high vision purposes, detecting low-level objects in an image is of great importance. These objects, which can be 2D or 3D, are called blobs. This Project describes segmentation, detection, and counting of objects as blobs in a real scenario. Detecting Multiple Objects Using Blob Detection For object recognition, navigation systems and surveillance systems, object tracking is an indispensable first-step. The conventional approach to object tracking is based on the difference between the current image and the background image. The algorithms based on the difference image are useful in extracting the moving objects from the image and track them in consecutive frames. The proposed algorithm, consisting of three stages i.e. background elimination, foreground detection and tracking using Gaussian Mixture Model. Blob Analysis is applied on an images, so as to observe the object size and counting of the object. Simulation has been done by MATLAB.