A Motion Detection Algorithm Using Local Phase Information

• Published in: Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 606 - 625
• Main Authors: Lazar, Aurel A., Zhou, Yiyin, Ukani, Nikul H.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Algorithms; Animals; Derivatives; Detectors; Digital signal processors; Fourier transforms; Humans; Intelligence; Models, Neurological; Motion; Motion capture; Motion detectors; Motion perception; Motion Perception - physiology; Phase transformations; Photoreceptors; Signal processing; Signal Processing, Computer-Assisted; Technology application; Temporal logic; Visual; Visual Pathways - physiology
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2016/7915245

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.