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TGSF/TLoG filter with optical flow techniques for large motion detection

Sarker M. H., Sloane A.

Machine Graphics and Vision

2007

Vol. 16, No. 3/4

237-249

In this paper, detection and segmentation of large motion in moving image sequences is presented. For detecting motion, the intensity of each pixel is convolved with the second derivative of the Temporal Gaussian Smoothing Function (TGSF) or the Temporal Laplacian of Gaussian (TLoG) filter. The zero-crossing in a single frame of the resulting function indicates the positions of moving edges. An intensity change over time due to a small illumination effect does not produce a zero crossing. Therefore, such changes are not interpreted as human motion by this method. The optical flow velocity is computed by using the spatial and temporal derivatives of this function, and it is normal to the zero crossing contours. Pixels belonging to the normal velocities are projected back to the original color image sequences to achieve a segmented color image. Experiments show that a moving object is detected correctly, and good segmentation results are achieved.

Segmentation based on homomorphic filtering and improved seeded region growing for mobile robots tracking in image sequences

Kasinski A. J., Hamdy A. M.

Machine Graphics and Vision

2001

Vol. 10, No. 4

447-466

In this paper, we present a method for extracting of mobile robots in a sequence of noisy frames, assuming a complex background composed of textured floor, illuminated unvenly. A homomorphic filter if used, as a preprocessor, to enhance the acquired frames by eliminating the illumination component and emphasizing the reflectance component of the image function. To speed up preprocessing of each frame, filtering is only applied to the pixels belonging to the regions of interest (ROI). In all the tested cases, homomorphic--filtering led to better results than those obtained without preprocessing. The segmentation process has been based on seeded region growing procedure for reconstructing the shape of the mobil robot. We proposed automatic seed points selection in the binarized difference image, and use an adaptive threshold. This use eliminates or at least considerably reduces false negative detections, and reduces sensitivity of aggregation results to the selected seed points as compared to the classical seeded region growing procedure. Additionally, by imposing a condition of strong connectivityu bettween a seed point and its neighborhood, aggregation of undesired pixels efficiently eliminates false positive detections. Implementation of segmentation and tracking can be run in real time. High tracking accuracy has been obtained through out all the frames in a test sequence.