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(转载)yolov4

(转载)yolov4

作者: 恰我年少时 | 来源:发表于2023-04-20 12:01 被阅读0次

    Deep SORT

    Introduction

    This repository contains code for Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT).
    We extend the original SORT algorithm to
    integrate appearance information based on a deep appearance descriptor.
    See the arXiv preprint for more information.

    Dependencies

    The code is compatible with Python 2.7 and 3. The following dependencies are
    needed to run the tracker:

    • NumPy
    • sklearn
    • OpenCV

    Additionally, feature generation requires TensorFlow (>= 1.0).

    Installation

    First, clone the repository:

    git clone https://github.com/nwojke/deep_sort.git
    

    Then, download pre-generated detections and the CNN checkpoint file from
    here.

    NOTE: The candidate object locations of our pre-generated detections are
    taken from the following paper:

    F. Yu, W. Li, Q. Li, Y. Liu, X. Shi, J. Yan. POI: Multiple Object Tracking with
    High Performance Detection and Appearance Feature. In BMTT, SenseTime Group
    Limited, 2016.
    

    We have replaced the appearance descriptor with a custom deep convolutional
    neural network (see below).

    Running the tracker

    The following example starts the tracker on one of the
    MOT16 benchmark
    sequences.
    We assume resources have been extracted to the repository root directory and
    the MOT16 benchmark data is in ./MOT16:

    python deep_sort_app.py \
        --sequence_dir=./MOT16/test/MOT16-06 \
        --detection_file=./resources/detections/MOT16_POI_test/MOT16-06.npy \
        --min_confidence=0.3 \
        --nn_budget=100 \
        --display=True
    

    Check python deep_sort_app.py -h for an overview of available options.
    There are also scripts in the repository to visualize results, generate videos,
    and evaluate the MOT challenge benchmark.

    Generating detections

    Beside the main tracking application, this repository contains a script to
    generate features for person re-identification, suitable to compare the visual
    appearance of pedestrian bounding boxes using cosine similarity.
    The following example generates these features from standard MOT challenge
    detections. Again, we assume resources have been extracted to the repository
    root directory and MOT16 data is in ./MOT16:

    python tools/generate_detections.py \
        --model=resources/networks/mars-small128.pb \
        --mot_dir=./MOT16/train \
        --output_dir=./resources/detections/MOT16_train
    

    The model has been generated with TensorFlow 1.5. If you run into
    incompatibility, re-export the frozen inference graph to obtain a new
    mars-small128.pb that is compatible with your version:

    python tools/freeze_model.py
    

    The generate_detections.py stores for each sequence of the MOT16 dataset
    a separate binary file in NumPy native format. Each file contains an array of
    shape Nx138, where N is the number of detections in the corresponding MOT
    sequence. The first 10 columns of this array contain the raw MOT detection
    copied over from the input file. The remaining 128 columns store the appearance
    descriptor. The files generated by this command can be used as input for the
    deep_sort_app.py.

    NOTE: If python tools/generate_detections.py raises a TensorFlow error,
    try passing an absolute path to the --model argument. This might help in
    some cases.

    Training the model

    To train the deep association metric model we used a novel cosine metric learning approach which is provided as a separate repository.

    Highlevel overview of source files

    In the top-level directory are executable scripts to execute, evaluate, and
    visualize the tracker. The main entry point is in deep_sort_app.py.
    This file runs the tracker on a MOTChallenge sequence.

    In package deep_sort is the main tracking code:

    • detection.py: Detection base class.
    • kalman_filter.py: A Kalman filter implementation and concrete
      parametrization for image space filtering.
    • linear_assignment.py: This module contains code for min cost matching and
      the matching cascade.
    • iou_matching.py: This module contains the IOU matching metric.
    • nn_matching.py: A module for a nearest neighbor matching metric.
    • track.py: The track class contains single-target track data such as Kalman
      state, number of hits, misses, hit streak, associated feature vectors, etc.
    • tracker.py: This is the multi-target tracker class.

    The deep_sort_app.py expects detections in a custom format, stored in .npy
    files. These can be computed from MOTChallenge detections using
    generate_detections.py. We also provide
    pre-generated detections.

    Citing DeepSORT

    If you find this repo useful in your research, please consider citing the following papers:

    @inproceedings{Wojke2017simple,
      title={Simple Online and Realtime Tracking with a Deep Association Metric},
      author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
      booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
      year={2017},
      pages={3645--3649},
      organization={IEEE},
      doi={10.1109/ICIP.2017.8296962}
    }
    
    @inproceedings{Wojke2018deep,
      title={Deep Cosine Metric Learning for Person Re-identification},
      author={Wojke, Nicolai and Bewley, Alex},
      booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
      year={2018},
      pages={748--756},
      organization={IEEE},
      doi={10.1109/WACV.2018.00087}
    }
    

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