The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Recovery of Ego-Motion Using Region Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Frame Estimation of Planar Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A master-slave system to acquire biometric imagery of humans at distance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
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Most moving object detection methods rely on approaches similar to background subtraction or frame differences that require camera to be fixed at a certain position. However, on mobile robots, a background model can not be maintained because of the camera motion introduced by the robot motion. To overcome such obstacle, some researchers proposed methods that use optical flow and stereo vision to detect moving objects on moving platforms. These methods work under a assumption that the areas belong to the interesting foreground moving objects are relatively small compare to the areas belong to the uninteresting background scene. However, in many situations, the moving objects may approach closely to the robot on which the camera is located. In such a case, the assumption of small foreground moving object will be violated. This paper presents a framework which shows that the small foreground moving object assumption could be relaxed. Further, it integrates the observations in motion field and image alignment to provide a robust moving object detection solution in unconstrained indoor environment.