Object detection and segmentation algorithm in complex dynamic scene

In complex conditions of dynamic scenes, it is difficult to detect and segment objects accurately in image sequence. According to the image characteristics of the object in complex conditions, we propose an object detection and segmentation model which was fused with scale invariant feature transform (SIFT) Flow characteristics in dynamic scene. Through analyzing the advantages of the movement characteristic information by SIFT Flow, and combining the color and brightness information in Commission Internationale de L'Eclairage (CIE) Lab, we establish a four-dimensional vector space. We utilize the improved multi-scale center-surround comparison method to generate salient map in each channel and fuse by linear superposition, then establish the dynamic scene saliency object model in image sequence. Finally, mean-shift clustering algorithm and morphology are used to achieve object segmentation accurately. Experimental results indicate that the proposed method can segment more complete object region than the traditional method in complex dynamic scenes and aerial video. And it also has good robustness and high segmentation accuracy.