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view tracking.cfg @ 811:429bb43e8278 opencv3
switching the branches to correct names (opencv3.1 is old code previously updated to OpenCV3 and default is now updated to OpenCV 2.4.13
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 10 Jun 2016 15:44:37 -0400 |
parents | 56153d439f8c |
children | f7cf43b5ad3b 21f10332c72b |
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# filename of the video to process video-filename = laurier.avi # filename of the database where results are saved database-filename = laurier.sqlite # filename of the homography matrix homography-filename = laurier-homography.txt # filename of the camera intrinsic matrix intrinsic-camera-filename = intrinsic-camera.txt # -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816 distortion-coefficients = -0.11759321 distortion-coefficients = 0.0148536 distortion-coefficients = 0.00030756 distortion-coefficients = -0.00020578 distortion-coefficients = -0.00091816 # undistorted image multiplication undistorted-size-multiplication = 1.31 # Interpolation method for remapping image when correcting for distortion: 0 for INTER_NEAREST - a nearest-neighbor interpolation; 1 for INTER_LINEAR - a bilinear interpolation (used by default); 2 for INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood; 3 for INTER_LANCZOS4 interpolation-method = 1 # filename of the mask image (where features are detected) mask-filename = none # undistort the video for feature tracking undistort = false # load features from database load-features = false # display trajectories on the video display = false # original video frame rate (number of frames/s) video-fps = 29.97 # number of digits of precision for all measurements derived from video # measurement-precision = 3 # first frame to process frame1 = 0 # number of frame to process: 0 means processing all frames nframes = 0 # feature tracking # maximum number of features added at each frame max-nfeatures = 1000 # quality level of the good features to track feature-quality = 0.0812219538558 # minimum distance between features (px) min-feature-distanceklt = 3.54964337411 # size of the block for feature characteristics (px) block-size = 7 # use of Harris corner detector use-harris-detector = false # k parameter to detect good features to track (OpenCV) k = 0.4 # size of the search window at each pyramid level (px) window-size = 6 # maximal pyramid level in the feature tracking algorithm pyramid-level = 5 # number of displacement to test minimum feature motion ndisplacements = 3 # minimum displacement to keep features (px) min-feature-displacement = 0.05 # maximum feature acceleration acceleration-bound = 3 # maximum feature deviation deviation-bound = 0.6 # number of frames to smooth positions (half window) smoothing-halfwidth = 5 # number of frames to compute velocities #nframes-velocity = 5 # maximum number of iterations to stop feature tracking max-number-iterations = 20 # minimum error to reach to stop feature tracking min-tracking-error = 0.183328975142 # minimum eigen value of a 2x2 normal matrix of optical flow equations min-feature-eig-threshold = 1e-4 # minimum length of a feature (number of frames) to consider a feature for grouping min-feature-time = 9 # Min Max similarity parameters (Beymer et al. method) # connection distance in feature grouping (world distance unit or px) mm-connection-distance = 2.68813545522 # segmentation distance in feature grouping (world distance unit or px) mm-segmentation-distance = 1.81511847456 # maximum distance between features for grouping (world distance unit or px) max-distance = 5 # minimum cosine of the angle between the velocity vectors for grouping min-velocity-cosine = 0.8 # minimum average number of features per frame to create a vehicle hypothesis min-nfeatures-group = 3.16747690802 # Road user classification # min number of pixels in cropped image to classify by SVM min-npixels-crop = 400 # method to aggregate road user speed speed-aggregation-method = median # number of frames to ignore at both ends of a series (noisy) nframes-ignore-at-ends = 2 # quantile for the speed aggregation, if quantile is chosen speed-aggregation-quantile = 50 # speed value below which all classes are equiprobable (distributions give odd values there) (km/h) min-speed-equiprobable = 3.33 # filename of the general ped/cyc/veh SVM classifier pbv-svm-filename = modelPBV.xml # filename of the cyc/veh SVM classifier bv-svm-filename = modelBV.xml # maximum pedestrian speed (agregate: mean, median, 85th centile, etc.) 10 km/h max-ped-speed = 10.0 # maximum cyclist speed (agregate: mean, median, 85th centile, etc.) 30 km/h (3xped) max-cyc-speed = 30.0 # mean pedestrian speed and standard deviation (in a normal distribution) 4.91+-0.88 km/h mean-ped-speed = 4.91 std-ped-speed = 0.88 # mean cyclist speed and standard deviation (in a log-normal distribution) 11.+-4.83 km/h cyc-speed-loc = 2.31 cyc-speed-scale = 0.42 # mean vehicle speed and standard deviation (in a normal distribution) 18.45+-7.6 km/h mean-veh-speed = 18.45 std-veh-speed = 7.6 # Safety analysis # maximum speed when predicting future motion (km/h) max-predicted-speed = 50 # time horizon for collision prediction (s) prediction-time-horizon = 5 # collision distance threshold (m) collision-distance = 1.8 # option to compute crossing zones and predicted PET crossing-zones = false # prediction method: cv, na, ps prediction-method = na # number of predicted trajectories (use depends on prediction method) npredicted-trajectories = 10 # maximum acceleration for normal adaptation input symmetric distribution (m/s2) max-normal-acceleration = 2 # maximum steering for normal adaptation input symmetric distribution (rad/s) max-normal-steering = 0.2 # minimum acceleration for input distribution (m/s2) (extreme values used for evasive action distributions) min-extreme-acceleration = -9.1 # maximum acceleration for input distribution (m/s2) (extreme values used for evasive action distributions) max-extreme-acceleration = 4.3 # maximum steering for input distribution (rad/s) (extreme values used for evasive action distributions) max-extreme-steering = 0.5 # use feature positions and velocities for prediction use-features-prediction = true