CUVI by Example

Motion Detection

CUVI library comes with all the image processing essentials that can be used to build countless applications. For example the Computer Vision module of CUVI can be used for motion and intrusion detection in a live video stream and tracking an object of interest throughout series of cameras installed in a premises. The processing pipeline for motion detection goes as follows:

• Read a frame from the camera stream
• Select Strong Features in that Frame using CUVI
• Read next frame
• Track features of first frame in the second frame using CUVI
• Set alarm if motion is detected

The CUVI functions used in this example are goodFeaturesToTrack() and trackFeatures(). For simplicity we have removed the I/O part on host side from the code

#include <cuvi.h> static const int width = 640; //Width of video frame static const int height = 480; //Height of video frame CuviFilter* f; // Declaring CUVI Filter //Parameters for feature selection static const int requestedFeatures = 150; //Number of features to look for static const float featureQuality = 0.006f; //Quality of a feature static const int featureMinDistance = 15; //Minimum distance between 2 features static const int blockSize = 3; //block size for computing Eigen Matrix static const float k = -2.0f; //k for Harris Corner detector //Parameters for feature tracking static const int pyramidLevels = 3; //Level Of Scaling static const CuviSize trackingWindow = cuviSize(30,30); //Size of tracking window static const float residue = 20.0f; //Absolute Difference Between Original Location Window & Tracked Location Window static const int iterations = 10; //Maximum number of iterations before a feature is found //Pre-processing parameters static const bool smoothBeforeSelecting = false; //Smooth Image Before Feature Selection & Tracking static const bool adjustImage = false; //Adjust Image Light Before Feature Selection //Post-processing parameters static const float movementThreshold = 0.33f; //Mark as motion if a feature moves 0.33 Pixels //Checks if the feature has moved from is original location. //It can be used in intrusion detection and the sensitivity can be set using 'threshold' parameter bool featureHasMoved(CuviPointValue2D point1, CuviPointValue2D point2, float threshold){ if(point2.val != 0.0f) return false; return ((fabsf(point1.x - point2.x)>threshold) || (fabsf(point1.y - point2.y)>threshold)); } void main() { //Creating a smoothing 3x3 Gaussian Filter with Standard Deviation 0.7 cuviCreateFilter(&f,3,3); f->sigma = 0.7f; cuviCreateFilterSpecial(f,CUVI_FILTER_GAUSSIAN); //Image size CuviSize size = cuviSize(width,height); //Buffer Images on GPU CuviImage* gFrame = new CuviImage(size,8,3); CuviImage* gimg1 = new CuviImage(size,8,1); CuviImage* gimg2 = new CuviImage(size,8,1); //Region of Interest in the video frame CuviROI roi = cuviROI(0,0,width,height); CuviPointValue2D *features1, *features2; int feature_count = 0; do { //Read a Video Frame on host 'frame' and populate GPU image with it gFrame->upload(frame->imageData,frame->widthStep); //Converting to Gray Image for computations cuvi::colorOperations::RGB2Gray(gFrame,gimg1); //Do the same with next, adjacent frame gFrame->upload(frame->imageData); cuvi::colorConvert(gFrame,gimg2); feature_count = RequestedFeatures; //Reset feature count to original //Use this option if the adjacent frames are lightening sensitive if(AdjustImage){ cuvi::colorOperations::adjust(gimg1); cuvi::colorOperations::adjust(gimg2); } //Use this option if the images contain fair amount of noise if(SmoothBeforeSelecting){ //Apply Gaussian Smoothing Filter On Both The Images cuvi::imageFiltering::imageFilter(gimg1,roi,f); cuvi::imageFiltering::imageFilter(gimg2,roi,f); } //Defining feature selection criteria from parameters CuviFeaturesCriteria feature_criteria = cuviFeaturesCriteria(CUVI_FEATURES_HARRIS, featureQuality, featureMinDistance, blockSize, k); //Call any Feature Detector on first Frame( KLT | HARRIS | PETER ) cuvi::computerVision::goodFeaturesToTrack(gimg1,roi,features1,&feature_count,feature_criteria); //Defining tracking criteria from tracking parameters CuviTrackingCriteria tracking_criteria = cuviTrackingCriteria(pyramidLevels, trackingWindow, iterations, residue); //Track Features Using of Frame#1 onto Frame#2 using KLT Tracker cuvi::trackFeatures(gimg1,gimg2,features1,features2,feature_count,tracking_criteria ); //At this point you can indetify whether the selected features in frame one moved in frame two or not for(int i=0; i<feature_count; i++){ //True only if the feature has moved from its location if(FeatureHasMoved(features1[i],features2[i],MovementThreshold)) //You can also plot the tracked features on the screen } }while(video_Frames) //Freeing GPU Memory delete gFrame; delete gimg1; delete gimg2; }

Here's an exact same example applied on a video feed of a webcam