Real Time Multi-target Visual Tracking based on Velocity Segmentation Technique

We propose a feature-based multi-target tracking algorithm which can track multiple targets in real time with a simple but efficient velocity segmentation method. The optical flow velocity distribution profile of the feature points detected on a moving target or on the background when the camera has ego-motion is assumed to have a Gaussian-like function. We can separate the background feature points and those of moving targets by examining the velocity distribution function profile at each frame without a prior knowledge on the number and texture of the moving objects. The optical flow velocity of a feature point in each image frame is computed by the iterative Lucas-Kanade algorithm. These feature points are divided into groups with the similar velocity. Feature points are further divided into sub-groups according to their proximity the image frame. The feature point group with the largest span is identified as the background feature and its velocity is the camera velocity when the background is fixed. Multiple targets are identified based on their velocities and proximity in one frame. With a pyramidal sampling scheme to reduce the frame size to one-sixteenth of its original size, and the iterative Lucas-Kanade algorithm to find the optical flow in a multi-resolution manner, we are able to track multiple objects in real time with a moving hand-held camera.