Optical Flow Features for Event Detection

Sammanfattning: In this thesis, we employ optical flow features for the detection of the rigid or non‐rigid single object on an input video. For optical flow estimation, we use the Point Line [PL] method [2] (as a local method) to estimate the motion of the image sequence which is generated from the input video stream. Although the Lukas and Kanade [LK] is a popular local method for estimation of the optical flow, it is weak in dealing with the linear symmetric images even by use of regularization [e.g. Tikhonov]. The PL method is more powerful than the LK method and can properly separate both line flow and point flow. For dealing with rapidly changing data in some part of an image (high motion problem), a gaussian pyramid with five levels (different image resolutions) is employed. In this way, the pyramid height (Level) must be chosen properly according to the maximum optical flow that we expect in each section of the image without iteration. After determining the best‐estimated optical flow vector for every pixel, the algorithm should detect an object on video with its direction to the right or left. By using techniques such as segmentation and averaging the magnitude of flow vectors the program can detect and distinguish rigid objects (e.g. a car) and non‐rigid objects (e.g. a human). Finally the algorithm makes a new video output that includes detected object with flow vectors, the pyramid levels map which has been used for optical flow estimation and a respective binary image.