Motion detection driven by visual attention mechanism applying chaos analysis method

A motion detection technology driven by visual attention mechanism applying chaos analysis method is proposed in this paper. The method firstly extracts salient regions of the scene image based on visual attention mechanism, and then detects motion objects with chaos analysis method on salient regions. The technical route of the motion detection technology driven by visual attention mechanism applying chaos analysis method is as follows: firstly, bottom image features that are sensitive to vision are extracted from scene image; secondly, salient map which reflects the visual saliency of each scene location is obtained via incorporating these image features according to the feature integration theory; and then, motion detection is done with the chaos analysis method on the salient image region which contains the most salient scene image location; finally, the next salient image region with the strongest saliency in the residual ones is detected according to the proximity criterion and the inhibition-of-return criterion; the process given above is repeated till having detected motion objects on all scene image regions. To decrease computational complexity, the traditional method of extracting salient image region is improved as follows: local standard deviation operator replaces center-surround operator to estimate local saliency of each image location and salient pixel clustering method replaces scale saliency rule to extract salient regions in our method. The chaos analysis method firstly estimates whether the joint histogram puts up chaotic characteristics, then classifies all scatters of the joint histogram that puts up chaotic characteristics by fractal dimension with a fixed threshold, and finally corresponds the classified result to salient regions to segment motion objects. Motion detection technology driven by visual attention mechanism is effective and robust. The contrast experiments and algorithm cost analysis are done which show that our method excels the motion detection method based on mosaics in segmentation effect and velocity.