Salient Foreground Segmentation For Gait Recognition

The advance of biometrics technology enables the computer to detect gait and gait abnormality on intelligent monitoring.However,the interception of crowds in the surveillance video will bring great difficulties to the foreground segmentation and tracking.The existing moving object extraction methods still have difficulty in robust segmenting the object,without considering that each independent target will have the motion continuity and the space-time structural consistency under the video.Aiming for this shortcoming,this thesis does the following work:First,the existing d work on foreground segmentation algorithm for gait recognition is introduced briefly.Five topics are presented:moving object extraction,optical flow field calculation,saliency detection,super pixel segmentation and gait recognition.Secondly,a novel gait recognition algorithm based on the constraints of both motion continuity and salient structure of foreground.The method uses optical flow clustering and saliency detection for both motion continuity and salient structure of foreground,which not considered in existing work.For the motion continuity,an estimation strategy for the foreground probability is introduced based on the clustering of optical flows.It clusters the regions based on optical flow and computes the foreground probability based on the area size.For the salient structural consistency,a scheme based on the saliency detection is presented.A energy minimization model is introduced by integrating the two strategies for a salient foreground area and consequently a robust gait recognition can be resulted.Finally,a novel gain recognition method based on salient foreground segmentation using a coarse-contour multi-feature voting scheme is proposed.First,a block-based gradient self-shadowing algorithm for texture statistics is proposed here,which can largely remove the shadow area of the sole of moving target.This algorithm extracts a more accurate motion area and eliminates soft shadows.Second,a salient foreground segmentation is obtained with a rough-contour based multi-feature voting which includes the spatial and temporal coherence constraints according to the human body features.Integrating these two methods into the former gait recognition method using constraints of motion continuity and saliency,a new gait recognition method is obtained.Experiments show that this novel method obtains a better recognition effect than the previous methods.