Computer Vision: GEI

Gait Engery Image (GEI) is the most commonly used feature in GAAP detection. The extraction method is simple, and it can also well represent the speed, shape, and other features of the Gait. It is defined as follows:

In the q-th gait sequence, the coordinates of the moment t's walking silhouette are the pixel values of (x, y.

The ratio of width to height of the walking silhouette can be used to determine the gait cycle. This value is easy and changes periodically with the gait state.

A single walking silhouette must be adjusted to W and H in width. The ratio of the silhouette remains unchanged during adjustment. That is, if the Silhouette itself is W'/H' <w/h, the silhouette is reduced to W * (W * H'/W, and placed in the vertical center of W * H.

Get the code for the rescaled walking silhouette:

// get resized gait imageif(!walk_img.empty()){vector<vector<Point> > contours;vector<Vec4i> hierarchy;Mat walk_img_tmp;threshold(walk_img,walk_img_tmp,128,255,THRESH_BINARY);findContours( walk_img_tmp, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );vector<vector<Point> > contours_poly( contours.size() );vector<Rect> boundRect( contours.size() );int maxRectHeight=0;int maxRectId=0;if(contours.size()>0){for( int i = 0; i< contours.size(); i++ ){//drawContours( walk_img, contours, i, Scalar(255,255,255), 2, 8, hierarchy, 0, Point() );//Approximates a polygonal curve(s) with the specified precision.approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );//Calculates the up-right bounding rectangle of a point set.boundRect[i] = boundingRect( Mat(contours_poly[i]) );if(boundRect[i].height>maxRectHeight){maxRectHeight = boundRect[i].height;maxRectId = i;}}//rectangle( walk_img, boundRect[maxRectId].tl(), boundRect[maxRectId].br(), Scalar(255,255,255), 2, 8, 0 );double aspect_ratio=(double)boundRect[maxRectId].height/boundRect[maxRectId].width;double base_aspect_ratio=(double)gei_height/gei_width;aspect_ratios.push_back(aspect_ratio);if(aspect_ratio>=base_aspect_ratio){Mat gait_roi=walk_img(boundRect[maxRectId]);Mat gait_roi_tmp;double resize_scale=double(gei_height)/gait_roi.rows;resize(gait_roi,gait_roi_tmp,Size(),resize_scale,resize_scale);Mat gait_img=Mat::zeros(gei_height,gei_width,CV_8UC1);for(int i=0;i<gei_height;i++){uchar* p_tmp=gait_roi_tmp.ptr<uchar>(i);uchar* p=gait_img.ptr<uchar>(i);for(int j=(gei_width-gait_roi_tmp.cols)/2,k=0;k<gait_roi_tmp.cols;k++,j++){p[j]=p_tmp[k];}}gait_imgs.push_back(gait_img);}else{Mat gait_roi=walk_img(boundRect[maxRectId]);Mat gait_roi_tmp;double resize_scale=double(gei_width)/gait_roi.cols;resize(gait_roi,gait_roi_tmp,Size(),resize_scale,resize_scale);Mat gait_img=Mat::zeros(gei_height,gei_width,CV_8UC1);int i=(gei_height-gait_roi_tmp.rows)/2;for(int k=0;k<gait_roi_tmp.rows;k++,i++){uchar* p_tmp=gait_roi_tmp.ptr<uchar>(k);uchar* p=gait_img.ptr<uchar>(i);for(int j=0;j<gei_width;j++){p[j]=p_tmp[j];}}gait_imgs.push_back(gait_img);}}

If you get GEI, you can obtain the weighted average of all graphs in each cycle of the previous step.

if(aspect_ratios.size()<4)break;// get gait feature: gait energy imagevector<int> max_ids;for(int i=2;i<aspect_ratios.size()-2;i++){if((aspect_ratios[i]>aspect_ratios[i-1])&&(aspect_ratios[i]>aspect_ratios[i-2])&&(aspect_ratios[i]>aspect_ratios[i+1])&&(aspect_ratios[i]>aspect_ratios[i+2]))max_ids.push_back(i);}// for all gait cyclesfor(int cycle_id=1;cycle_id<max_ids.size();cycle_id++){int gait_start_id = max_ids[cycle_id-1];int gait_end_id = max_ids[cycle_id]-1;Mat gait_energy_img=Mat::zeros(gei_height,gei_width, CV_32F);if(gait_end_id-gait_start_id>=6 && gait_end_id-gait_start_id<30){for(int g=gait_start_id;g<=gait_end_id;g++){Mat gait=gait_imgs[g];Mat gait_tmp;gait.convertTo(gait_tmp,CV_32F);gait_energy_img = gait_energy_img+gait_tmp;#ifdef GAIT_DEBUGchar tmp[50];itoa(g,tmp,10);imshow(tmp,gait);#endif}//waitKey(10000);gait_energy_img = gait_energy_img/(float)(gait_end_id-gait_start_id+1);for(int r=0;r<gait_energy_img.rows;r++){float* p=gait_energy_img.ptr<float>(r);for(int c=0;c<gait_energy_img.cols;c++)feature_out<<p[c]<<" ";}feature_out<<endl;label_out<<people_id_iter<<" "<<walk_condition_iter_iter<<endl;cout<<"gait feature cycle #"<<cycle_id-1<<endl;#ifdef GAIT_DEBUGMat gait_enery_img_show;gait_energy_img.convertTo(gait_enery_img_show,CV_8UC1);imshow("GEI",gait_enery_img_show);waitKey(10000);#endif}}}

On the CASIA Dataset B Dataset, obtain the GEI diagram of each angle: