Uses Correntropy to Separate Moving Objects in Foreground from Non-Stationary Background in Video Sequences
This filter utilizes hidden state estimation with non-Gaussian uncertainties to separate and elucidate moving objects from a non-stationary background in video sequences. For example, in video surveillance applications, it is very important to detect new moving objects entering the breadth of the camera and separate foreground objects from the background allowing the possibility to detect sudden changes in the scene or enable the machine to track a moving object or objects. Typically, the Kalman Filter provides the most accurate background estimation; however, this estimation takes only random Gaussian variables into account. Thus, Kalman Filter doesn’t perform well in non-Gaussian settings, exposing a need for a new filter that can extract higher order information from signals. University of Florida researchers have designed an adaptive background estimation for video sequences that is based on the use of correntropy instead of the conventional mean squared error, allowing the devised filter to use higher order statistics. This filter also can be employed in background modeling in real-time sports footage to extract foreground objects and in monitoring traffic on highways and roads.
Filters moving objects from non-stationary background to detect sudden changes and track moving objects
- Adapts quickly to any illumination change or addition or removal of objects to the background, allowing for a more rapid and efficient change between video frames
- Adapts estimation quickly, improving image quality greatly by reducing the presence of unwanted non-Gaussian noise such as salt and pepper noise in video sequences
- Devised filter uses higher order statistical information allowing this filter to be applied to color images in video sequences like real time sports footage or surveillance
This filter using hidden state estimation proposes a computable function based on statistical theory dealing with the limits and efficiency of information processing. The function utilizes the similarity measure correntropy as a performance index, which is directly related to the probability of how similar two random variables are in a neighborhood of joint space. Researchers at the University of Florida assumed that there was a filter for each pixel of the video sequence, and that each pixel is defined by three continuous hidden states or color values. An identity matrix within the function uses these hidden states to classify the background and separate objects from the background. This identity matrix does not expect the states to change because the background color doesn’t change rapidly. Initially the background can be set in the first frame, and then the filter will work with each incoming frame unsupervised. As a result the filter manages to extract the background, eliminate noise, and adapt to the changes in the background scene.
The following videos demonstrate using correntropy to separate moving objects in foreground from non-stationary background in video sequences. In both videos, the upper left window is the original video feed, the lower right window is the estimated background, and the upper right window is the difference between these two windows that correspond to the foreground objects.