diff --git a/CMakeLists.txt b/CMakeLists.txt
index 225c9e1..f305500 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -80,6 +80,9 @@ target_link_libraries(test_rtinference tkDNN)
 add_executable(detection demo/detection/detection.cpp)
 target_link_libraries(detection tkDNN)
 
+add_executable(live demo/live/live.cpp)
+target_link_libraries(live tkDNN)
+
 #install
 if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
     set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" 
diff --git a/README.md b/README.md
index 0f08d93..557736a 100644
--- a/README.md
+++ b/README.md
@@ -33,3 +33,17 @@ Assumiung you have correctly builded the library these are the test ready to exe
 * test_yolo: YOLO detection network (CUDNN and TENSORRT)
 * test_yolo_tiny: smaller version of YOLO (CUDNN and TENSRRT)
 
+## Live detection
+For the live detection you need to precompile the tensorRT file by luncing the desidered network test, this is the recommended process:
+```
+export TKDNN_MODE=FP16   # set the half floating point optimization
+rm yolo.rt		 # be sure to delete(or move) old tensorRT files
+./test_yolo              # run the yolo test (is slow)
+# with f16 inference the result will be a bit incorrect
+```
+this will genereate a yolo.rt file that can be used for live detection:
+```
+./live yolo.rt 1 -s -t0.3    # launch detection on device 1 with 0.3 thresh
+```
+
+
diff --git a/demo/live/live.cpp b/demo/live/live.cpp
new file mode 100644
index 0000000..5c1a833
--- /dev/null
+++ b/demo/live/live.cpp
@@ -0,0 +1,184 @@
+#include<iostream>
+#include "tkdnn.h"
+#include <stdlib.h>     /* srand, rand */
+#include <unistd.h>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+const char *reg_bias = "../tests/yolo/layers/g31.bin";
+
+int prob_sort(const void *pa, const void *pb) {
+    tkDNN::box a = *(tkDNN::box *)pa;
+    tkDNN::box b = *(tkDNN::box *)pb;
+    float diff = a.prob - b.prob;
+    if(diff < 0) return 1;
+    else if(diff > 0) return -1;
+    return 0;
+}
+
+cv::Mat GetSquareImage(const cv::Mat& img, int target_width) {
+    int width = img.cols, height = img.rows;
+
+    cv::Mat square = cv::Mat::zeros( target_width, target_width, img.type() );
+
+    int max_dim = ( width >= height ) ? width : height;
+    float scale = ( ( float ) target_width ) / max_dim;
+    cv::Rect roi;
+    if ( width >= height )
+    {
+        roi.width = target_width;
+        roi.x = 0;
+        roi.height = height * scale;
+        roi.y = ( target_width - roi.height ) / 2;
+    }
+    else
+    {
+        roi.y = 0;
+        roi.height = target_width;
+        roi.width = width * scale;
+        roi.x = ( target_width - roi.width ) / 2;
+    }
+
+    cv::resize( img, square( roi ), roi.size() );
+
+    return square;
+}
+
+//return inference time
+double compute_image( cv::Mat imageORIG, 
+                    tkDNN::NetworkRT *netRT, tkDNN::RegionInterpret *rI,
+                    dnnType *input, dnnType *output) {
+    TIMER_START
+
+    //Resize with padding and convert to float
+    cv::Mat image = GetSquareImage(imageORIG, netRT->input_dim.w);
+    cv::Mat imageF;
+    image.convertTo(imageF, CV_32FC3, 1/255.0); 
+
+    //split channels
+    cv::Mat bgr[3];   //destination array
+    cv::split(imageF,bgr);//split source
+    
+    //write channels
+    int idx = 0;
+    memcpy((void*)&input[idx], (void*)bgr[2].data, imageF.rows*imageF.cols*sizeof(dnnType));
+    idx = imageF.rows*imageF.cols;
+    memcpy((void*)&input[idx], (void*)bgr[1].data, imageF.rows*imageF.cols*sizeof(dnnType));
+    idx *= 2;    
+    memcpy((void*)&input[idx], (void*)bgr[0].data, imageF.rows*imageF.cols*sizeof(dnnType));
+
+    //DO INFERENCE
+    checkCuda(  cudaMemcpyAsync(netRT->buffersRT[netRT->buf_input_idx], input, 
+                                netRT->input_dim.tot()*sizeof(float), 
+                                cudaMemcpyHostToDevice, netRT->stream));
+    netRT->enqueue();
+    checkCuda(  cudaMemcpyAsync(output, netRT->buffersRT[netRT->buf_output_idx], 
+                                netRT->output_dim.tot()*sizeof(float), 
+                                cudaMemcpyDeviceToHost, netRT->stream));
+    cudaStreamSynchronize(netRT->stream);
+    
+    
+
+    rI->interpretData(output, imageORIG.cols, imageORIG.rows);
+
+    TIMER_STOP
+    return t_ns;
+}
+
+
+
+int print_usage() {
+    std::cout<<"usage: ./live net.rt camera_idx\n";
+    return 1;
+}
+
+
+
+
+int main(int argc, char *argv[]) {
+
+    //params
+    char *tensor_path = NULL;
+    int   device      = 0;
+    float thresh = 0.3f;
+    bool  show = false;
+
+    //parse params
+    int c;
+    while ((c = getopt (argc, argv, "t:si:")) != -1) {
+        switch(c) {
+        case 't':   thresh = atof(optarg);       break;
+        case 's':   show = true;                 break;
+        case '?':   
+            return print_usage();
+        default:    return print_usage();
+        }
+    }
+
+    if(argc - optind == 2) {
+        tensor_path   = argv[optind];
+        device        = atoi(argv[optind+1]);
+    } else {
+        std::cout<<"not enough arguments.\n";
+        return print_usage();
+    }
+    //end parsing
+
+    std::cout<<"open video stream on device: "<<device<<"\n";
+    cv::VideoCapture cap(device); 
+    if(!cap.isOpened())
+        FatalError("unable to open video stream");
+    cap.set(CV_CAP_PROP_BUFFERSIZE, 1); // process only last frame
+
+    if(!fileExist(tensor_path))
+        FatalError("unable to read serialRT file");
+
+    //convert network to tensorRT
+    tkDNN::NetworkRT netRT(NULL, tensor_path);
+    tkDNN::RegionInterpret rI(netRT.input_dim, netRT.output_dim, 80, 4, 5, thresh, reg_bias);
+
+    dnnType *input = new float[netRT.input_dim.tot()];
+    dnnType *output = new float[netRT.output_dim.tot()];
+
+    double mTime = 0;
+    int processed_images = 0;
+
+
+    for(;;) {
+        
+        //LOAD IMAGE
+        cv::Mat img; //= cv::imread("../demo/live/test.jpeg", CV_LOAD_IMAGE_COLOR);
+        cap >> img;   
+
+	if(!img.data)                              
+            FatalError("Could not open image");
+        std::cout<<"Image size: ("<<img.cols<<"x"<<img.rows<<")\n";
+
+        mTime += compute_image(img, &netRT, &rI, input, output); 
+
+        qsort(rI.res_boxes, rI.res_boxes_n, sizeof(tkDNN::box), prob_sort);
+        for(int i=0; i<rI.res_boxes_n; i++) {
+            tkDNN::box bx = rI.res_boxes[i];
+            std::cout<<" ("<<int(bx.prob*100)<<"%) "<<bx.cl
+                     <<": "<<bx.x<<" "<<bx.y<<" "<<bx.w<<" "<<bx.h<<"\n";
+
+            cv::rectangle(img, cv::Point(bx.x - bx.w/2, bx.y - bx.h/2), 
+                               cv::Point(bx.x + bx.w/2, bx.y + bx.h/2),
+            cv::Scalar( 0, 0, 255), 2);
+        }
+
+        //show results
+        if(show) {
+            cv::namedWindow("result");
+            cv::imshow("result", img);
+            cv::waitKey(1);
+        }
+
+	processed_images++;   
+	std::cout<<"mean time per frames: "<<mTime/processed_images/1000<<" ms\n"<<"\n"; 
+    }
+
+    return 0;
+}
diff --git a/demo/live/test.jpeg b/demo/live/test.jpeg
new file mode 100644
index 0000000..b8e0810
Binary files /dev/null and b/demo/live/test.jpeg differ