Abstract
A novel algorithm to extract moving objects from video sequences is proposed in this paper. The proposed algorithm employs a flashing system to obtain an alternate series of lit and unlit frames from a single camera. For each unlit frame, the proposed algorithm synthesizes the corresponding lit frame using a motion-compensated interpolation scheme. Then, by comparing the unlit frame with the lit frame, we construct the sensitivity map, which provides depth cues. In addition to the sensitivity term, color, coherence, and smoothness terms are employed to define an energy function, which is minimized to yield segmentation results. Moreover, we develop a faster version of the proposed algorithm, which reduces the computational complexity significantly at the cost of slight performance degradation. Experiments on various test sequences show that the proposed algorithm provides high-quality segmentation results.
Highlights
Due to the advances in computation and communication technologies, the interest in video contents has increased significantly, and it has become more and more important to analyze and understand video contents automatically using computer vision techniques
We extend the image segmentation techniques in [19, 20], which use a pair of flash and no-flash images, to the video segmentation case
The depth information of objects and the background can be inferred from a sensitivity map [28, 29], which represents the ratio of the amounts of light reaching each pixel p in the unlit frame and the lit frame
Summary
Due to the advances in computation and communication technologies, the interest in video contents has increased significantly, and it has become more and more important to analyze and understand video contents automatically using computer vision techniques. They extract objects in the first frame based on EURASIP Journal on Advances in Signal Processing users’ markings, and track the objects in subsequent frames using color, position, and temporal cues These semiautomatic methods [10,11,12,13] can achieve relatively accurate segmentation results using initial interactions. In [17], Zhang et al proposed estimating the depth information of sparse points to detect foreground objects These automatic methods [14,15,16,17] are effective, provided that objects and the background exhibit different motion characteristics. We propose a novel algorithm to extract objects as well as humans from video sequences automatically.
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