A Fast Traffic Video Stabilization Method Based on Trajectory Derivatives

• Main Authors: Minda Zhao, Sibin Deng, Qiang Ling
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Video stabilization; trajectory derivative; feedback-based strategy
• Online Access: https://ieeexplore.ieee.org/document/8624247/

This paper aims to solve the stabilization problem of traffic videos, which are mostly captured by the cameras mounted on the vehicles. Compared with normal videos captured with handheld cameras, traffic videos often face more challenges, such as dynamic scenes, dominant foreground objects, and significant parallax. Conventional methods often regard videos stabilization as an optimization problem with complex constraints and thus cost much computation time. To address the above issues, we propose a fast method by making use of trajectory derivatives at adjacent frames. When the parallax is not serious or the inter-frame time is short enough, the non-rigid characteristic of camera jitter can be ignored. Then, we simplify the perspective transformation to affine transformation between adjacent frames, i.e., the trajectory derivative holds linear with respect to the position of the trajectory. We combine our method with a feedback-based foreground trajectory judgment strategy and significantly speed up the processing speed. The new algorithm can satisfy the real-time requirement, which is critical for real applications, at the cost of tolerable stabilization performance degradation.