针对运动摄像机的快速低存储开销运动目标检测算法

摄像机的运动会导致整幅图像的运动，使得此情形下的目标检测极具挑战性。针对该问题提出一种快速低存储开销检测算法。首先，利用一种快速低存储开销配准方法计算相邻两帧的单应变换矩阵。而后，使用单应变换矩阵进行相邻两帧之间的配准，并由帧间差分获取帧间运动信息。最后，采用积累运动信息的方式构造不断更新的运动图像，通过对此运动图像进行阈值分割分离出最终的运动目标。在多个不同视频序列下的实验表明该算法能够有效地从嘈杂的场景中检测出运动目标。此外，与先前算法相比，该算法检测性能更好，且显著地降低了存储开销与计算时间开销。对于480360的序列而言，该算法需要的存储开销仅为825 kByte，且运算速度达到16帧/m。

Fast and memory-saving algorithm for moving object detection from a moving camera

It is challenging to detect moving objects from a moving camera as a motion field in the entire image can be induced by the camera motion. A fast and memory-saving detection method is proposed to resolve this problem. First, a fast and memory-saving registration scheme is used to estimate the homography transform between two neighboring frames. Then, neighboring frames is registered with the estimated transform, and frame-to-frame difference is performed to capture the motion cue. Finally, the motion cues are aggregated to construct a constantly updated motion image. After thresholding the motion image, separation of moving objects from the background is achieved. The effectiveness of the proposed method in detecting moving objects from cluttered scenes is validated via experiments on several different video sequences. In addition, this method performs better than previous techniques, while using a fraction of the computation time and a fraction of the memory as well. Specifically, with a memory usage of 825 kByte only, this method runs at 16 frames per second for a sequence with an image resolution of 480360.