\uc815\ud655\ud55c \ub9e4\uce6d:<\/strong> Kalman Filter\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21 \uacbd\ub85c\uc640 \uc2e4\uc81c \uacbd\ub85c\ub97c \ube44\uad50\ud558\uc5ec \ub9e4\uce6d\uc744 \uacb0\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n3. YOLO\uc640 Deep SORT \uacb0\ud569\ud558\uae30<\/h2>\n
\n YOLO\uc640 Deep SORT\ub97c \uacb0\ud569\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. YOLO\ub97c \ud1b5\ud574 \uac1d\uccb4\ub97c \uac80\ucd9c\ud55c \ud6c4, \uac01 \uac1d\uccb4\uc5d0 \ub300\ud574 Deep SORT\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd94\uc801\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \ud1b5\ud574 \uc2e4\uc2dc\uac04\uc73c\ub85c \ub2e4\uc911 \uac1d\uccb4\ub97c \ucd94\uc801\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
3.1 \ud658\uacbd \uc124\uc815<\/h3>\n
\n Python\uacfc OpenCV, TensorFlow \ub610\ub294 PyTorch \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec YOLO\uc640 Deep SORT\ub97c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n <\/p>\n
pip install opencv-python\npip install numpy\npip install tensorflow # \ub610\ub294 pytorch<\/code><\/pre>\n3.2 YOLO \ubaa8\ub378 \ub85c\ub4dc \ubc0f \uac1d\uccb4 \uac80\ucd9c<\/h3>\n
\n YOLO \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \uac1d\uccb4\ub97c \uac80\ucd9c\ud558\ub294 \uae30\ubcf8 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n <\/p>\n
import cv2\nimport numpy as np\n\n# YOLO \ubaa8\ub378 \ub85c\ub4dc\ndef load_yolo():\n net = cv2.dnn.readNet(\"yolov3.weights\", \"yolov3.cfg\")\n layer_names = net.getLayerNames()\n output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n return net, output_layers\n\n# \uac1d\uccb4 \uac80\ucd9c \ud568\uc218\ndef detect_objects(img, net, output_layers):\n blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n net.setInput(blob)\n outputs = net.forward(output_layers)\n \n class_ids, confidences, boxes = [], [], []\n \n for output in outputs:\n for detection in output:\n scores = detection[5:]\n class_id = np.argmax(scores)\n confidence = scores[class_id]\n if confidence > 0.5: # \uc2e0\ub8b0\ub3c4 \uae30\uc900\n center_x, center_y = int(detection[0] * img.shape[1]), int(detection[1] * img.shape[0])\n w, h = int(detection[2] * img.shape[1]), int(detection[3] * img.shape[0])\n x, y = int(center_x - w \/ 2), int(center_y - h \/ 2)\n boxes.append([x, y, w, h])\n confidences.append(float(confidence))\n class_ids.append(class_id)\n \n return boxes, confidences, class_ids<\/code><\/pre>\n3.3 Deep SORT \ucd08\uae30\ud654<\/h3>\n
\n Deep SORT\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574 \ucd08\uae30\ud654\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4. Deep SORT\ub294 KNX(\ud68c\uc804\ud558\ub294 \uae30\ubc18 \uc608\uce21)\uc640 \ub370\uc774\ud130 \uc218\uc9d1\uc744 \ud1b5\ud574 \ub9e4\uce6d\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n <\/p>\n
# Deep SORT \ucd08\uae30\ud654\nfrom deep_sort import DeepSort\n\ndeepsort = DeepSort() # Deep SORT \uac1d\uccb4 \uc0dd\uc131<\/code><\/pre>\n3.4 \uac1d\uccb4 \ucd94\uc801 \ucf54\ub4dc \uc791\uc131<\/h3>\n
\n YOLO\ub85c \uac80\ucd9c\ub41c \uac1d\uccb4\uc5d0 \ub300\ud574 Deep SORT\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd94\uc801\ud558\ub294 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n <\/p>\n
def track_objects(frame, net, output_layers, deepsort):\n boxes, confidences, class_ids = detect_objects(frame, net, output_layers)\n \n indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n for i in indices:\n box = boxes[i]\n x, y, w, h = box\n # \uae4a\uc740 SORT\uc5d0 \ud560\ub2f9\n deepsort.update(frame, box) # Deep SORT \uc5c5\ub370\uc774\ud2b8\n\n return frame # \ucd94\uc801\ub41c \uac1d\uccb4\uac00 \uc788\ub294 \ud504\ub808\uc784 \ubc18\ud658<\/code><\/pre>\n4. \uc804\uccb4 \ucf54\ub4dc \ud1b5\ud569<\/h2>\n
\n \uc704 \ub2e8\uacc4\ub97c \ubaa8\ub450 \ud1b5\ud569\ud558\uc5ec \uc804\uccb4 \ucf54\ub4dc\ub97c \uc791\uc131\ud569\ub2c8\ub2e4. \uc774 \ucf54\ub4dc\ub294 \uce74\uba54\ub77c\ub85c\ubd80\ud130 \ube44\ub514\uc624\ub97c \uc77d\uace0 YOLO\uc640 Deep SORT\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \ucd94\uc801\ud569\ub2c8\ub2e4.\n <\/p>\n
cap = cv2.VideoCapture(0) # \uc6f9\ucea0 \uc0ac\uc6a9\n\nnet, output_layers = load_yolo()\n\nwhile True:\n ret, frame = cap.read()\n if not ret:\n break\n \n frame = track_objects(frame, net, output_layers, deepsort)\n cv2.imshow(\"Object Tracking\", frame)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc744 \ud1b5\ud574 \uac1d\uccb4\ub97c \uac80\ucd9c\ud558\uace0, Deep SORT\uc640 \uacb0\ud569\ud558\uc5ec \ub2e4\uc911 \uac1d\uccb4 \ucd94\uc801 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. YOLO\uc640 Deep SORT\uc758 \uc870\ud569\uc740 \ube60\ub974\uace0 \uc815\ud655\ud55c \uac1d\uccb4 \ucd94\uc801\uc744 \uac00\ub2a5\ud558\uac8c \ud558\uc5ec \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc790\uc728\uc8fc\ud589\ucc28, \uac10\uc2dc \uc2dc\uc2a4\ud15c, \uc2a4\ud3ec\uce20 \ubd84\uc11d \ub4f1\uc5d0\uc11c \ud070 \ud6a8\uacfc\ub97c \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
YOLO(You Only Look Once)\ub294 \uc2e4\uc2dc\uac04 \uac1d\uccb4 \uac80\ucd9c\uc744 \uc704\ud55c \uac15\ub825\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. YOLO\ub294 \uc774\ubbf8\uc9c0\ub97c \ud55c \ubc88\uc758 \uc2e0\uacbd\ub9dd \ud1b5\uacfc\ub85c \ubaa8\ub4e0 \uac1d\uccb4\ub97c \uac80\ucd9c\ud558\uba70, \uc774\ub97c \ud1b5\ud574 \ube60\ub978 \ucc98\ub9ac \uc18d\ub3c4\ub97c \uc790\ub791\ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc744 \uc774\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \uac80\ucd9c\ud558\uace0, Deep SORT(Deep Learning based SORT)\uc640 \uacb0\ud569\ud558\uc5ec \ud6a8\uacfc\uc801\uc778 \ub2e4\uc911 \uac1d\uccb4 \ucd94\uc801 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. YOLO \ubaa8\ub378\uc758 \uc774\ud574 YOLO\ub294 \ub2e8\uc77c \ud68c\uadc0 … \ub354 \ubcf4\uae30 “YOLO \ubaa8\ub378\uc744 \uc774\uc6a9\ud55c \uac1d\uccb4 \ucd94\uc801\uacfc \ub2e4\uc911 \uac1d\uccb4 \uac80\ucd9c, \ub525 \uc18c\ud2b8(Deep SORT)\uc640\uc758 \uacb0\ud569\uc744 \ud1b5\ud55c \ub2e4\uc911 \uac1d\uccb4 \ucd94\uc801 \uc2dc\uc2a4\ud15c \uad6c\ucd95”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[189],"tags":[],"class_list":["post-38869","post","type-post","status-publish","format-standard","hentry","category-yolo"],"yoast_head":"\nYOLO \ubaa8\ub378\uc744 \uc774\uc6a9\ud55c \uac1d\uccb4 \ucd94\uc801\uacfc \ub2e4\uc911 \uac1d\uccb4 \uac80\ucd9c, \ub525 \uc18c\ud2b8(Deep SORT)\uc640\uc758 \uacb0\ud569\uc744 \ud1b5\ud55c \ub2e4\uc911 \uac1d\uccb4 \ucd94\uc801 \uc2dc\uc2a4\ud15c \uad6c\ucd95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/38869\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"YOLO \ubaa8\ub378\uc744 \uc774\uc6a9\ud55c \uac1d\uccb4 \ucd94\uc801\uacfc \ub2e4\uc911 \uac1d\uccb4 \uac80\ucd9c, \ub525 \uc18c\ud2b8(Deep SORT)\uc640\uc758 \uacb0\ud569\uc744 \ud1b5\ud55c \ub2e4\uc911 \uac1d\uccb4 \ucd94\uc801 \uc2dc\uc2a4\ud15c \uad6c\ucd95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"YOLO(You Only Look Once)\ub294 \uc2e4\uc2dc\uac04 \uac1d\uccb4 \uac80\ucd9c\uc744 \uc704\ud55c \uac15\ub825\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. YOLO\ub294 \uc774\ubbf8\uc9c0\ub97c \ud55c \ubc88\uc758 \uc2e0\uacbd\ub9dd \ud1b5\uacfc\ub85c \ubaa8\ub4e0 \uac1d\uccb4\ub97c \uac80\ucd9c\ud558\uba70, \uc774\ub97c \ud1b5\ud574 \ube60\ub978 \ucc98\ub9ac \uc18d\ub3c4\ub97c \uc790\ub791\ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc744 \uc774\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \uac80\ucd9c\ud558\uace0, Deep SORT(Deep Learning based SORT)\uc640 \uacb0\ud569\ud558\uc5ec \ud6a8\uacfc\uc801\uc778 \ub2e4\uc911 \uac1d\uccb4 \ucd94\uc801 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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