%YAML:1.0 ################################################################################ # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. ################################################################################ NvDCF: # [General] useUniqueID: 0 # Use 64-bit long Unique ID when assignining tracker ID. Default is [true] maxTargetsPerStream: 50 # Max number of targets to track per stream. Recommended to set >10. Note: this value should account for the targets being tracked in shadow mode as well. Max value depends on the GPU memory capacity # [Feature Extraction] useColorNames: 1 # Use ColorNames feature useHog: 1 # Use Histogram-of-Oriented-Gradient (HOG) feature useHighPrecisionFeature: 1 # Use high-precision in feature extraction. Default is [true] # [DCF] filterLr: 0.15 # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0] filterChannelWeightsLr: 0.22 # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0] gaussianSigma: 0.75 # Standard deviation for Gaussian for desired response when creating DCF filter [pixels] featureImgSizeLevel: 3 # Size of a feature image. Valid range: {1, 2, 3, 4, 5}, from the smallest to the largest SearchRegionPaddingScale: 1 # Search region size. Determines how large the search region should be scaled from the target bbox. Valid range: {1, 2, 3}, from the smallest to the largest # [MOT] [False Alarm Handling] maxShadowTrackingAge: 30 # Max length of shadow tracking (the shadow tracking age is incremented when (1) there's detector input yet no match or (2) tracker confidence is lower than minTrackerConfidence). Once reached, the tracker will be terminated. probationAge: 3 # Once the tracker age (incremented at every frame) reaches this, the tracker is considered to be valid earlyTerminationAge: 1 # Early termination age (in terms of shadow tracking age) during the probation period. If reached during the probation period, the tracker will be terminated prematurely. # [Tracker Creation Policy] [Target Candidacy] minDetectorConfidence: -1 # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking minTrackerConfidence: 0.75 # If the confidence of an object tracker is lower than this on the fly, then it will be tracked in shadow mode. Valid Range: [0.0, 1.0] minTargetBboxSize: 10 # If the width or height of the bbox size gets smaller than this threshold, the target will be terminated. minDetectorBboxVisibilityTobeTracked: 0.0 # If the detector-provided bbox's visibility (i.e., IOU with image) is lower than this, it won't be considered. minVisibiilty4Tracking: 0.0 # If the visibility of the tracked object (i.e., IOU with image) is lower than this, it will be terminated immediately, assuming it is going out of scene. # [Tracker Termination Policy] targetDuplicateRunInterval: 5 # The interval in which the duplicate target detection removal is carried out. A Negative value indicates indefinite interval. Unit: [frames] minIou4TargetDuplicate: 0.9 # If the IOU of two target bboxes are higher than this, the newer target tracker will be terminated. # [Data Association] Matching method useGlobalMatching: 0 # If true, enable a global matching algorithm (i.e., Hungarian method). Otherwise, a greedy algorithm wll be used. # [Data Association] Thresholds in matching scores to be considered as a valid candidate for matching minMatchingScore4Overall: 0.0 # Min total score minMatchingScore4SizeSimilarity: 0.5 # Min bbox size similarity score minMatchingScore4Iou: 0.1 # Min IOU score minMatchingScore4VisualSimilarity: 0.2 # Min visual similarity score minTrackingConfidenceDuringInactive: 1.0 # Min tracking confidence during INACTIVE period. If tracking confidence is higher than this, then tracker will still output results until next detection # [Data Association] Weights for each matching score term matchingScoreWeight4VisualSimilarity: 0.8 # Weight for the visual similarity (in terms of correlation response ratio) matchingScoreWeight4SizeSimilarity: 0.0 # Weight for the Size-similarity score matchingScoreWeight4Iou: 0.1 # Weight for the IOU score matchingScoreWeight4Age: 0.1 # Weight for the tracker age # [State Estimator] useTrackSmoothing: 1 # Use a state estimator stateEstimatorType: 1 # The type of state estimator among { moving_avg:1, kalman_filter:2 } # [State Estimator] [MovingAvgEstimator] trackExponentialSmoothingLr_loc: 0.5 # Learning rate for new location trackExponentialSmoothingLr_scale: 0.3 # Learning rate for new scale trackExponentialSmoothingLr_velocity: 0.05 # Learning rate for new velocity # [State Estimator] [Kalman Filter] kfProcessNoiseVar4Loc: 0.1 # Process noise variance for location in Kalman filter kfProcessNoiseVar4Scale: 0.04 # Process noise variance for scale in Kalman filter kfProcessNoiseVar4Vel: 0.04 # Process noise variance for velocity in Kalman filter kfMeasurementNoiseVar4Trk: 9 # Measurement noise variance for tracker's detection in Kalman filter kfMeasurementNoiseVar4Det: 9 # Measurement noise variance for detector's detection in Kalman filter # [Past-frame Data] useBufferedOutput: 0 # Enable storing of past-frame data in a buffer and report it back # [Instance-awareness] useInstanceAwareness: 0 # Use instance-awareness for multi-object tracking lambda_ia: 2 # Regularlization factor for each instance maxInstanceNum_ia: 4 # The number of nearby object instances to use for instance-awareness