Real-time image-based motion detection using color and structure
Motion detection is an important problem in computer vision and has multiple applications in the real world, including surveillance-related activities, gaming, and human-robot interactions. These systems need to be robust enough to handle fluctuations in light intensity and other external factors like noise and compression artifacts. In this thesis a method is proposed for detecting the regions of motion from a video sequence in real time. The main idea of this work is to detect motion based on both structure and color. Structure-based detection is carried out using information from the Census Transform computed on gradient images obtained with Sobel operators. The Census Transform characterizes local intensity patterns in an image. Color-based detection is done using color histograms, which allow efficient characterization without prior assumptions about color distribution in the scene. The probabilities obtained from the gradient-based Census Transform and Color Histograms are combined in a robust way to detect the active motion zones. Experimental results demonstrate the effectiveness of this approach. This thesis also presents an application for motion detection in foveal visual systems. Foveation reduces power and bandwidth requirements in a system. But the main challenge is to identify the region of interest that must be transmitted in high-resolution format while maintaining the rest in low resolution. In this work this issue is addressed, by proposing motion detection as a cue to select the region of interest in a given scene. Preliminary results are also presented to show the successful application of this approach.
Chakraborty, Manali, "Real-time image-based motion detection using color and structure" (2009). ETD Collection for University of Texas, El Paso. AAI1473855.