Unsupervised temporal commonality discovery
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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We present a computationally efficient and robust method for temporally calibrating video sequences from unsynchronized cameras by using object trajectories. Existing methods remain restricted in terms of their assumptions, and/or they are computationally expensive. To match and align the object trajectories, and thus to recover the frame offset between video sequences, we present an algorithm that is based on the Longest Consecutive Common Subsequence. The candidate frame offsets are obtained from each matched trajectory pair, and then a confidence check is performed. The algorithm is robust against possible errors due to background subtraction and location extraction, and can handle large frame offsets. We present experimental results for different frame offset values on different video sequences, which show the robustness of the algorithm in recovering the frame offsets. We also compare the presented algorithm with our previous work to demonstrate the computational efficiency provided.