diff --git a/person_detection/person_detection.py b/person_detection/person_detection.py
new file mode 100644
index 0000000..037734c
--- /dev/null
+++ b/person_detection/person_detection.py
@@ -0,0 +1,273 @@
+"""
+SPDX-FileCopyrightText: 2020-present Open Networking Foundation <info@opennetworking.org>
+SPDX-License-Identifier: LicenseRef-ONF-Member-1.01
+"""
+
+from __future__ import print_function
+
+import logging as log
+import os
+import sys
+import time
+from argparse import ArgumentParser, SUPPRESS
+
+import cv2
+from imutils import build_montages
+from openvino.inference_engine import IECore
+
+
+def build_argparser():
+    parser = ArgumentParser(add_help=False)
+    args = parser.add_argument_group('Options')
+    args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
+    args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",
+                      required=True, type=str)
+    args.add_argument("-i", "--input",
+                      help="Required. Path to video file or image. 'cam' for capturing video stream from camera",
+                      required=True, type=str)
+    # args.add_argument("-i2", "--input2",
+    #                   help="Optional. Path to second video file or image. 'cam' for capturing video stream from camera",
+    #                   default=None, type=str)
+    args.add_argument("-l", "--cpu_extension",
+                      help="Optional. Required for CPU custom layers. Absolute path to a shared library with the "
+                           "kernels implementations.", type=str, default=None)
+    args.add_argument("-pp", "--plugin_dir", help="Optional. Path to a plugin folder", type=str, default=None)
+    args.add_argument("-d", "--device",
+                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is "
+                           "acceptable. The demo will look for a suitable plugin for device specified. "
+                           "Default value is CPU", default="CPU", type=str)
+    args.add_argument("--labels", help="Optional. Path to labels mapping file", default=None, type=str)
+    args.add_argument("-pt", "--prob_threshold", help="Optional. Probability threshold for detections filtering",
+                      default=0.5, type=float)
+    args.add_argument("-ns", help='No show output', action='store_true')
+
+    return parser
+
+
+def main():
+    log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
+    args = build_argparser().parse_args()
+    model_xml = args.model
+    model_bin = os.path.splitext(model_xml)[0] + ".bin"
+    # Plugin initialization for specified device and load extensions library if specified
+    log.info("Initializing plugin for {} device...".format(args.device))
+    # plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
+    # if args.cpu_extension and 'CPU' in args.device:
+    #   plugin.add_cpu_extension(args.cpu_extension)
+    # Read IR
+    log.info("Reading IR...")
+    net = IECore().read_network(model=model_xml, weights=model_bin)
+
+    assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
+    assert len(net.outputs) == 1, "Demo supports only single output topologies"
+    input_blob = next(iter(net.inputs))
+    out_blob = next(iter(net.outputs))
+
+    # input_blob2 = next(iter(net.inputs))
+    # out_blob2 = next(iter(net.outputs))
+
+    log.info("Loading IR to the plugin...")
+    # exec_net = IECore().load_network(network=net, device_name=args.device, num_requests=2)
+    exec_net = IECore().load_network(network=net, device_name=args.device, num_requests=1)
+    # Read and pre-process input image
+    n, c, h, w = net.inputs[input_blob].shape
+    # n2, c2, h2, w2 = net.inputs[input_blob2].shape
+    del net
+    if args.input == 'cam':
+        input_stream = 0
+    elif args.input == 'gstreamer':
+        # gst rtp sink
+        input_stream = 'udpsrc port=5000 caps = " application/x-rtp, encoding-name=JPEG,payload=26" ! rtpjpegdepay ! decodebin ! videoconvert ! appsink'
+        #input_stream = 'udpsrc port=5000 caps = "application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264, payload=(int)96" ! rtph264depay ! decodebin ! videoconvert ! appsink'
+    else:
+        input_stream = args.input
+        assert os.path.isfile(args.input), "Specified input file doesn't exist"
+
+    if input_stream == 'gstreamer':
+        cap = cv2.VideoCapture(input_stream, cv2.CAP_GSTREAMER)
+    else:
+        cap = cv2.VideoCapture(input_stream)
+
+    # if args.input2 == 'cam':
+    #     input_stream2 = 0
+    # elif args.input2 == 'gstreamer':
+    #     input_stream2 = 'udpsrc port=5001 caps = " application/x-rtp, encoding-name=JPEG,payload=26" ! rtpjpegdepay ! decodebin ! videoconvert ! appsink'
+    # else:
+    #     input_stream2 = args.input2
+    #     assert os.path.isfile(args.input2), "Specified input file doesn't exist"
+    if args.labels:
+        with open(args.labels, 'r') as f:
+            labels_map = [x.strip() for x in f]
+    else:
+        labels_map = None
+
+    # if input_stream2 == 'gstreamer':
+    #     cap2 = cv2.VideoCapture(input_stream2, cv2.CAP_GSTREAMER)
+    # else:
+    #     cap2 = cv2.VideoCapture(input_stream2)
+
+    cur_request_id = 0
+    next_request_id = 1
+
+    # cur_request_id2 = 1
+    # next_request_id2 = 0
+
+    log.info("Starting inference in async mode...")
+    log.info("To switch between sync and async modes press Tab button")
+    log.info("To stop the demo execution press Esc button")
+
+    # Async doesn't work if True
+    # Request issues = Runtime Error: [REQUEST BUSY]
+    is_async_mode = False
+    render_time = 0
+    ret, frame = cap.read()
+    # ret2, frame2 = cap2.read()
+
+    # Montage width and height
+    # In this case means 2x1 boxes
+    mW = 2
+    mH = 1
+
+    frameList = []
+
+    print("To close the application, press 'CTRL+C' or any key with focus on the output window")
+    # while cap.isOpened() or cap2.isOpened():
+    while cap.isOpened():
+        if is_async_mode:
+            ret, next_frame = cap.read()
+            # ret2, next_frame2 = cap2.read()
+        else:
+            ret, frame = cap.read()
+            # ret2, frame2 = cap2.read()
+        #if not (ret and ret2):
+        if not ret:
+            break
+        initial_w = cap.get(3)
+        initial_h = cap.get(4)
+        # initial_w2 = cap2.get(3)
+        # initial_h2 = cap2.get(4)
+        # Main sync point:
+        # in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
+        # in the regular mode we start the CURRENT request and immediately wait for it's completion
+        inf_start = time.time()
+        if is_async_mode:
+            # if ret and ret2:
+            if ret:
+                in_frame = cv2.resize(next_frame, (w, h))
+                in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                in_frame = in_frame.reshape((n, c, h, w))
+                exec_net.start_async(request_id=next_request_id, inputs={input_blob: in_frame})
+
+                # in_frame2 = cv2.resize(next_frame2, (w2, h2))
+                # in_frame2 = in_frame2.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                # in_frame2 = in_frame2.reshape((n2, c2, h2, w2))
+                # exec_net.start_async(request_id=next_request_id2, inputs={input_blob2: in_frame2})
+
+        else:
+            # if (ret and ret2):
+            if ret:
+                in_frame = cv2.resize(frame, (w, h))
+                in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                in_frame = in_frame.reshape((n, c, h, w))
+                exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
+
+                # in_frame2 = cv2.resize(frame2, (w2, h2))
+                # in_frame2 = in_frame2.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                # in_frame2 = in_frame2.reshape((n2, c2, h2, w2))
+                # exec_net.start_async(request_id=cur_request_id2, inputs={input_blob2: in_frame2})
+
+        # if exec_net.requests[cur_request_id].wait(-1) == 0 and exec_net.requests[cur_request_id2].wait(-1) == 0:
+        if exec_net.requests[cur_request_id].wait(-1) == 0:
+            inf_end = time.time()
+            det_time = inf_end - inf_start
+
+            # Parse detection results of the current request
+            res = exec_net.requests[cur_request_id].outputs[out_blob]
+            # res2 = exec_net.requests[cur_request_id2].outputs[out_blob2]
+
+            for obj in res[0][0]:
+                # Draw only objects when probability more than specified threshold
+                if obj[2] > args.prob_threshold:
+                    xmin = int(obj[3] * initial_w)
+                    ymin = int(obj[4] * initial_h)
+                    xmax = int(obj[5] * initial_w)
+                    ymax = int(obj[6] * initial_h)
+                    class_id = int(obj[1])
+                    # Draw box and label\class_id
+                    color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
+                    cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
+                    det_label = labels_map[class_id] if labels_map else str(class_id)
+                    cv2.putText(frame, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7),
+                                cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
+                    print('Object detected, class_id:', class_id, 'probability:', obj[2], 'xmin:', xmin, 'ymin:', ymin,
+                          'xmax:', xmax, 'ymax:', ymax)
+
+            # for obj in res2[0][0]:
+            #     # Draw only objects when probability more than specified threshold
+            #     if obj[2] > args.prob_threshold:
+            #         xmin = int(obj[3] * initial_w2)
+            #         ymin = int(obj[4] * initial_h2)
+            #         xmax = int(obj[5] * initial_w2)
+            #         ymax = int(obj[6] * initial_h2)
+            #         class_id = int(obj[1])
+            #         # Draw box and label\class_id
+            #         color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
+            #         cv2.rectangle(frame2, (xmin, ymin), (xmax, ymax), color, 2)
+            #         det_label = labels_map[class_id] if labels_map else str(class_id)
+            #         cv2.putText(frame2, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7),
+            #                     cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
+            #         print('Object detected, class_id:', class_id, 'probability:', obj[2], 'xmin:', xmin, 'ymin:', ymin,
+            #               'xmax:', xmax, 'ymax:', ymax)
+
+            # Draw performance stats
+            inf_time_message = "Inference time: Not applicable for async mode" if is_async_mode else \
+                "Inference time: {:.3f} ms".format(det_time * 1000)
+            render_time_message = "OpenCV rendering time: {:.3f} ms".format(render_time * 1000)
+            if is_async_mode:
+                async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id)
+            else:
+                async_mode_message = "Async mode is off. Processing request {}".format(cur_request_id)
+
+            cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
+            cv2.putText(frame, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
+            cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
+                        (10, 10, 200), 1)
+
+            # cv2.putText(frame2, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
+            # cv2.putText(frame2, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
+            # cv2.putText(frame2, async_mode_message, (10, int(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
+            #             (10, 10, 200), 1)
+
+        render_start = time.time()
+
+        if not args.ns:
+            # if ret and ret2:
+            if ret:
+            #     frameList.append(frame)
+            #     # frameList.append(frame2)
+            # montages = build_montages(frameList, (640, 480), (mW, mH))
+            # for montage in montages:
+            #     cv2.imshow("Detection results", montage)
+                cv2.imshow("Detection results", frame)
+            render_end = time.time()
+            render_time = render_end - render_start
+
+        if is_async_mode:
+            cur_request_id, next_request_id = next_request_id, cur_request_id
+
+            frame = next_frame
+            # frame2 = next_frame2
+        key = cv2.waitKey(1)
+        if key == 27:
+            break
+        if 9 == key:
+            is_async_mode = not is_async_mode
+            log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))
+
+    cap.release()
+    # cap2.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    sys.exit(main() or 0)