Multitarget-tracker/data/settings_coco.ini at master · 167182/Multitarget-tracker · GitHub

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[detection]

#-----------------------------
# opencv_dnn = 12
# darknet_cudnn = 10
# tensorrt = 11
detector_backend = 10

#-----------------------------
# Target and backend for opencv_dnn detector
# DNN_TARGET_CPU
# DNN_TARGET_OPENCL
# DNN_TARGET_OPENCL_FP16
# DNN_TARGET_MYRIAD
# DNN_TARGET_CUDA
# DNN_TARGET_CUDA_FP16
ocv_dnn_target = DNN_TARGET_CPU

# DNN_BACKEND_DEFAULT
# DNN_BACKEND_HALIDE
# DNN_BACKEND_INFERENCE_ENGINE
# DNN_BACKEND_OPENCV
# DNN_BACKEND_VKCOM
# DNN_BACKEND_CUDA
# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
ocv_dnn_backend = DNN_BACKEND_OPENCV

#-----------------------------
# nn_weights = data/coco/yolov7.onnx
# nn_config = data/coco/yolov7.onnx

# nn_weights = data/coco/yolov6s.onnx
# nn_config = data/coco/yolov6s.onnx

nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/yolov4.weights
nn_config = C:/work/home/mtracker/Multitarget-tracker/data/yolov4.cfg

class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco.names

#-----------------------------
confidence_threshold = 0.2

max_crop_ratio = -1
max_batch = 1
gpu_id = 0

#-----------------------------
# YOLOV3
# YOLOV4
# YOLOV5
net_type = YOLOV4

#-----------------------------
# INT8
# FP16
# FP32
inference_precision = FP32

#-----------------------------
# Detect only set of types, ";"
white_list =

#-----------------------------
# For TensorRT optimization, bytes
video_memory = 0;


[tracking]

#-----------------------------
# DistCenters = 0   // Euclidean distance between centers, pixels
# DistRects = 1     // Euclidean distance between bounding rectangles, pixels
# DistJaccard = 2   // Intersection over Union, IoU, [0, 1]
# DistHist = 3      // Bhatacharia distance between histograms, [0, 1]

distance_type = 0

#-----------------------------
# KalmanLinear = 0
# KalmanUnscented = 1

kalman_type = 0

#-----------------------------
# FilterCenter = 0
# FilterRect = 1

filter_goal = 0

#-----------------------------
# TrackNone = 0
# TrackKCF = 1
# TrackMIL = 2
# TrackMedianFlow = 3
# TrackGOTURN = 4
# TrackMOSSE = 5
# TrackCSRT = 6
# TrackDAT = 7
# TrackSTAPLE = 8
# TrackLDES = 9
# Used if filter_goal == FilterRect

lost_track_type = 0

#-----------------------------
# MatchHungrian = 0
# MatchBipart = 1

match_type = 0

#-----------------------------
# Use constant acceleration motion model:
# 0 - unused (stable)
# 1 - use acceleration in Kalman filter (experimental)
use_aceleration = 0

#-----------------------------
# Delta time for Kalman filter
delta_time = 0.4

#-----------------------------
# Accel noise magnitude for Kalman filter
accel_noise = 0.2

#-----------------------------
# Distance threshold between region and object on two frames
dist_thresh = 0.8

#-----------------------------
# If this value > 0 than will be used circle with this radius
# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
min_area_radius_pix = -1

#-----------------------------
# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
min_area_radius_k = 0.8

#-----------------------------
# If the object do not assignment more than this frames then it will be removed
max_skip_frames = 50

#-----------------------------
# The maximum trajectory length
max_trace_len = 50

#-----------------------------
# Detection abandoned objects
detect_abandoned = 0
# After this time (in seconds) the object is considered abandoned
min_static_time = 5
# After this time (in seconds) the abandoned object will be removed
max_static_time = 25
# Speed in pixels. If speed of object is more that this value than object is non static
max_speed_for_static = 10