FastMOT

News

• (2021.2.13) Support Scaled-YOLOv4 models
• (2021.1.3) Add DIoU-NMS for YOLO (+1% MOTA)
• (2020.11.28) Docker container provided on Ubuntu 18.04

Description

FastMOT is a custom multiple object tracker that implements:

• YOLO detector
• SSD detector
• Deep SORT + OSNet ReID
• KLT optical flow tracking
• Camera motion compensation

Deep learning models are usually the bottleneck in Deep SORT, making Deep SORT unusable for real-time applications. FastMOT significantly speeds up the entire system to run in real-time even on Jetson. It also provides enough flexibility to tune the speed-accuracy tradeoff without a lightweight model.

To achieve faster processing, FastMOT only runs the detector and feature extractor every N frames. Optical flow is used to fill in the gaps. YOLOv4 was trained on CrowdHuman (82% [email protected]) while SSD's are pretrained COCO models from TensorFlow. OSNet outperforms the original feature extractor in Deep SORT. FastMOT also re-identifies targets that moved out of frame and will keep the same IDs.

Both detector and feature extractor use the TensorRT backend and perform asynchronous inference. In addition, most algorithms, including Kalman filter, optical flow, and data association, are optimized using Numba.

Performance

Results on MOT20 train set

Detector Skip	MOTA	MOTP	IDF1	IDS	MT	ML
N = 1	63.3%	72.8%	54.2%	5821	867	261
N = 5	61.4%	72.2%	55.7%	4517	778	302

FPS on MOT17 sequences

Sequence	Density	FPS
MOT17-13	5 - 30	38
MOT17-04	30 - 50	22
MOT17-03	50 - 80	15

Performance is evaluated with YOLOv4 using py-motmetrics. Note that neither YOLOv4 nor OSNet was trained or finetuned on the MOT20 dataset, so train set results should generalize well. FPS results are obtained on Jetson Xavier NX.

FastMOT has MOTA scores close to state-of-the-art trackers from the MOT Challenge. Tracking speed can reach up to 38 FPS depending on the number of objects. On a desktop CPU/GPU, FPS is expected to be much higher. More lightweight models can be used to achieve better tradeoff.

Requirements

• CUDA >= 10
• cuDNN >= 7
• TensorRT >= 7
• OpenCV >= 3.3
• PyCuda
• Numpy >= 1.15
• Scipy >= 1.5
• TensorFlow < 2.0 (for SSD support)
• Numba == 0.48
• cython-bbox

Install for Ubuntu 18.04

Make sure to have nvidia-docker installed. The image requires an NVIDIA Driver version >= 450. Build and run the docker image:

$ docker build -t fastmot:latest .
$ docker run --rm --gpus all -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY fastmot:latest

Install for Jetson Nano/TX2/Xavier NX/Xavier

Make sure to have JetPack 4.4+ installed and run the script:

$ scripts/install_jetson.sh

Download models

This includes both pretrained OSNet, SSD, and my custom YOLOv4 ONNX model

$ scripts/download_models.sh

Build YOLOv4 TensorRT plugin

Modify compute here to match your GPU compute capability for x86 PC

$ cd fastmot/plugins
$ make

Download VOC dataset for INT8 calibration

Only required if you want to use SSD

$ scripts/download_data.sh

Usage

• USB webcam:

  $ python3 app.py --input_uri /dev/video0 --mot
  

• MIPI CSI camera:

  $ python3 app.py --input_uri csi://0 --mot
  

• RTSP stream:

  $ python3 app.py --input_uri rtsp://<user>:<password>@<ip>:<port>/<path> --mot
  

• HTTP stream:

  $ python3 app.py --input_uri http://<user>:<password>@<ip>:<port>/<path> --mot
  

• Image sequence:

  $ python3 app.py --input_uri img_%06d.jpg --mot
  

• Video file:

  $ python3 app.py --input_uri video.mp4 --mot
  

• Use --gui to visualize and --output_uri to save output
• To disable the GStreamer backend, set WITH_GSTREAMER = False here
• Note that the first run will be slow due to Numba compilation

More options can be configured in cfg/mot.json

• Set resolution and frame_rate that corresponds to the source data or camera configuration (optional). They are required for image sequence, camera sources, and MOT Challenge evaluation. List all configurations for your USB/CSI camera:

  $ v4l2-ctl -d /dev/video0 --list-formats-ext
  

• To change detector, modify detector_type. This can be either YOLO or SSD
• To change classes, set class_ids under the correct detector. Default class is 1, which corresponds to person
• To swap model, modify model under a detector. For SSD, you can choose from SSDInceptionV2, SSDMobileNetV1, or SSDMobileNetV2
• Note that with SSD, the detector splits a frame into tiles and processes them in batches for the best accuracy. Change tiling_grid to [2, 2], [2, 1], or [1, 1] if a smaller batch size is preferred
• If more accuracy is desired and processing power is not an issue, reduce detector_frame_skip. Similarly, increase detector_frame_skip to speed up tracking at the cost of accuracy. You may also want to change max_age such that max_age × detector_frame_skip ≈ 30

Track custom classes

FastMOT supports multi-class tracking and can be easily extended to custom classes (e.g. vehicle). You need to train both YOLO and a ReID model on your object classes. Check Darknet for training YOLO and fast-reid for training ReID. After training, convert the model to ONNX format and place it in fastmot/models. To convert YOLO to ONNX, use tensorrt_demos to be compatible with the TensorRT YOLO plugins.

Add custom YOLOv3/v4

1. Subclass YOLO like here: https://github.com/GeekAlexis/FastMOT/blob/4e946b85381ad807d5456f2ad57d1274d0e72f3d/fastmot/models/yolo.py#L94

   ENGINE_PATH: path to TensorRT engine (converted at runtime)
   MODEL_PATH: path to ONNX model
   NUM_CLASSES: total number of classes
   LETTERBOX: keep aspect ratio when resizing
              For YOLOv4-csp/YOLOv4x-mish, set to True
   NEW_COORDS: new_coords parameter for each yolo layer
               For YOLOv4-csp/YOLOv4x-mish, set to True
   INPUT_SHAPE: input size in the format "(channel, height, width)"
   LAYER_FACTORS: scale factors with respect to the input size for each yolo layer
                  For YOLOv4/YOLOv4-csp/YOLOv4x-mish, set to [8, 16, 32]
                  For YOLOv3, set to [32, 16, 8]
                  For YOLOv4-tiny/YOLOv3-tiny, set to [32, 16]
   SCALES: scale_x_y parameter for each yolo layer
           For YOLOv4-csp/YOLOv4x-mish, set to [2.0, 2.0, 2.0]
           For YOLOv4, set to [1.2, 1.1, 1.05]
           For YOLOv4-tiny, set to [1.05, 1.05]
           For YOLOv3, set to [1., 1., 1.]
           For YOLOv3-tiny, set to [1., 1.]
   ANCHORS: anchors grouped by each yolo layer
   

   Note that anchors may not follow the same order in the Darknet cfg file. You need to mask out the anchors for each yolo layer using the indices in mask in Darknet cfg. Unlike YOLOv4, the anchors are usually in reverse for YOLOv3 and tiny
2. Change class labels here to your object classes
3. Modify cfg/mot.json: set model in yolo_detector to the added Python class and set class_ids you want to detect. You may want to play with conf_thresh based on the accuracy of your model

Add custom ReID

1. Subclass ReID like here: https://github.com/GeekAlexis/FastMOT/blob/aa707888e39d59540bb70799c7b97c58851662ee/fastmot/models/reid.py#L51

   ENGINE_PATH: path to TensorRT engine (converted at runtime)
   MODEL_PATH: path to ONNX model
   INPUT_SHAPE: input size in the format "(channel, height, width)"
   OUTPUT_LAYOUT: feature dimension output by the model (e.g. 512)
   METRIC: distance metric used to match features ('euclidean' or 'cosine')
   

2. Modify cfg/mot.json: set model in feature_extractor to the added Python class. You may want to play with max_feat_cost and max_reid_cost - float values from 0 to 2, based on the accuracy of your model

Citation

If you find this repo useful in your project or research, please star and consider citing it:

@software{yukai_yang_2020_4294717,
 author       = {Yukai Yang},
 title        = {{FastMOT: High-Performance Multiple Object Tracking
                  Based on YOLO, Deep SORT, and Optical Flow}},
 month        = nov,
 year         = 2020,
 publisher    = {Zenodo},
 version      = {v1.0.0},
 doi          = {10.5281/zenodo.4294717},
 url          = {https://doi.org/10.5281/zenodo.4294717}
}