one of the main problems of object tracking is identifying the object to be tracked. from my previous POST, i selected the object manually
Object detection is a necessary step for any tracking algorithm because the algorithm needs to identify the moving object. The objective of object detection is to find out the position of the object and the region it occupies in the first frame.
This task is extremely difficult due to the complex shapes of the moving targets. In order to simplify the problem, a variety of constrains are imposed to help in finding the object in image plane, such as the number of objects to be tracked, types of objects, etc. Currently, there are three popular techniques used in object tracking: image segmentation, background subtraction and supervised learning algorithms.
PROBLEMS
background subtraction is used for fixed cameras for moving camera we can use SIFT to stabilize the camera and perform background subtraction. However we do not get 100% stability with this method.
The image segmentation methods are able to partition the image into perceptually similar regions, but the criteria for a good partition and the efficiency are two problems that the image segmentation algorithms need to address.
The main drawback for supervised learning methods is that they usually require a large collection of samples from each object class and the samples must be manually labeled.
SOURCE : Tracking of Multiple Objects under Partial Occlusion - Bing Han, Christopher Paulson, Taoran Lu, Dapeng Wu, Jian Li - 2010
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