Incremental motion estimation through modified bundle adjustment

Zhengyou Zhang, Ying Shan

Abstract: An incremental motion estimation scheme for long image sequence analysis is introduced. It applies to a sliding window of triplet images and maintains local motion consistency without resort to post-concatenation. This is possible thanks to our newly developed local process called three-view partial bundle adjustment. Unlike previous approaches that rely only on point matches across three or more views, our local process also takes into account all available two-view matches, leading to more accurate motion estimation. For sparse image sequences, two-view matches are very reliable and it becomes even more important to use them since the number of matches across more views decreases quickly. In this case, our incremental method can produce results very close to those obtained with a global bundle adjustment but in a fraction of time. Experiments with both synthetic and real data have been conducted to compare the proposed technique with other techniques, and have shown our technique to be clearly superior.