Obtaining a dynamically reconstructed background sprite image is an important and challenging task for video applications such as object-based video coding, tracking and video object segmentation. This paper proposes a novel system for video background reconstruction with camera in motion.Firstly, the proposed algorithm uses feature point pairs from current frame and a reference frame for reliable Global Motion Estimation (GME) where a dynamic reference frame update strategy is utilized. Once the global motion transformation matrix is obtained, each frame is warped into a projective plane where a statistic-based background modeling algorithm is applied and the background sprite image is produced. Compared with conventional sprite generation methods, the proposed algorithm:(1) provides efficient and reliable GME using feature points;(2) significantly reduces the accumulated warping error by a dynamic reference frame update mechanism; (3)produces accurate background pixel reconstruction using a statistic analytical model. The experimental results are presented and analyzed to show the robustness and accuracy of the proposed system.

Avoiding potential safety hazard is the primary task of vision-based assistant driving system(ADS). Potential safety hazard exists in driving individual vehicles. Although these hazards are unexpected, obvious characteristics exist for vehicles that make them happen, such as: relatively fast speed, changing lanes frequently and being occluded as shuttling in the busy traffic. All these characteristics go against on-road tracking for the unsafe vehicle. At present, the assistant driving system is only permitted in the field of obstracle detection and location. However, those systems are not involved in tracking of vehicles with potential safety hazard. The paper presents an approach to tracking and online learning of on-road vehicles with potential safety hazard. Further, we improve the method of online learning to the unsafe hazard.The performance of our tracking algorithm is evaluated on a public benchmark with test data from various challenging videos on different conditions. The experiment results demonstrate that, in the same condition, our method can obtain samples more efficiently and lead the classifier to converge more quickly.

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