Performance results of traffic detection and tracking with Yolov8
This test demonstrates the traffic detection and a short video of a crossroad with Yolov8 BoT-SORT and ByteTrack See
. Video is with resolution (1920 x 1080), 10 seconds length and 25 FPS, taken with ordinary camera. This means that any FPS bigger that 25 is Ok for a realtime video stream processing with Yolov8.
The result is output video with same Full HD resolution (1920 x 1080) with bounding boxes and detection scores.
First, install PyTorch with GPU support, and then ultralytics:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install ultralytics
A small Yolov8 model (yolov8s.pt) is trained for 400 epochs with random frames of a longer video over six types of traffic vechicles:
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:03<0
all 105 3436 0.996 0.99 0.994 0.936
car 105 2976 0.996 0.962 0.989 0.914
bus 105 21 1 0.981 0.995 0.974
minibus 105 203 0.998 1 0.995 0.992
truck 105 75 0.993 1 0.995 0.978
van 105 126 0.997 1 0.995 0.941
motorbike 105 35 0.994 1 0.995 0.819
Here is shown how to draw the output from the Yolov8 predict and track methods on the video frames. I have choosen several random colors for the bounding boxes.
import numpy as np
import cv2
def drawBox(image, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
(x,y), (w,h) = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
cv2.rectangle(image, (x,y), (w,h), color, thickness=1, lineType=cv2.LINE_AA)
if label:
t_w, t_h = cv2.getTextSize(label, 0, 0.5, thickness=1)[0] # text width, height
outside = y - t_h >= 3
(w,h) = x + t_w, y - t_h - 3 if outside else y + t_h + 3
cv2.rectangle(image, (x,y), (w,h), color, -1, cv2.LINE_AA) # filled
cv2.putText(image,
label, (x, y - 2 if outside else y + t_h + 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
txt_color,
thickness=1,
lineType=cv2.LINE_AA)
def drawBoxes(image, boxes, labels=[], colors=[], score=True, conf=None):
#Define labels
if labels == []:
labels = {0: u'car', 1: u'bus', 2: u'minibus',3: u'truck', 4: u'van', 5: u'motorbike'}
#Define colors
if colors == []:
colors = [(89, 161, 197),(57,76,139),(19, 222, 24),(186, 55, 2),(167, 146, 11),(190, 76, 98), (139,71,93),(84,139,84)]
#plot each boxes
for box in boxes:
#add score in label if score=True
if score :
label = labels[int(box[-1])] + " " + str(round(100 * float(box[-2]),1)) + "%"
else :
label = labels[int(box[-1])]
#filter every box under conf threshold if conf threshold setted
if conf is None or box[-2] > conf :
# print(box)
idx = int(box[-1]) % len(colors)
color = colors[idx]
drawBox(image, box, label, color)This is the main cycle processing the video, I use OpenCV for the input and output video streams. The output ia a video file with bounding boxes labels and scores, written to a file vid_res.mp4. Average FPS is calculated as FPS sum divided on frames number, it includes only the Yolov8 detection.
# process a test video and prints average FPS
import math
import random
import os
import cv2
import numpy as np
import time
import argparse
import configparser
from ultralytics import YOLO
import draw_boxes as Boxes
video_file = 'vid.mp4'
video_file_out = 'vid_res.mp4'
model = YOLO('best_m.pt')
vid = cv2.VideoCapture(video_file)
fps = int(round(vid.get(cv2.CAP_PROP_FPS)))
w = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) # float
h = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out_w = w
out_h = h
out = cv2.VideoWriter(video_file_out, fourcc, fps, (out_w, out_h), True)
print( "image size: " , w, " x ", h)
np.set_printoptions(suppress=True)
frame = 0
average_fps = 0.0
while True:
ret, img = vid.read()
if not ret:
break
start = time.time()
# output = model.track(img)
# output = model.track(img, conf=0.25, tracker="bytetrack.yaml")
output = model.predict(img, conf=0.25)
end = time.time()
frame = frame + 1
fps = 1.0/(end-start)
print("%.1f" % fps)
average_fps += fps
Boxes.drawBoxes( img, output[0].boxes.data, score=True)
out.write(img)
print("Average FPS: %.1f" % (average_fps / frame) )
vid.release()
out.release()Result frames:
Performance results of RTX 3060 are shown below. I obtained these results with running the test several times for each detection method (commented lines) and taking the maximal Average FPS:
Here is the video output for detection without tracking:
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