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 for Ubuntu

Description

FastMOT is a custom multiple object tracker that implements:

YOLO detector
SSD detector
Deep SORT + OSNet ReID
KLT tracker
Camera motion compensation

Deep SORT requires running detection and feature extraction sequentially, which often becomes a bottleneck for real-time applications. FastMOT significantly speeds up the entire system to run in real-time even on Jetson. Motion compensation improves tracking for non-stationary camera where Deep SORT/FairMOT usually fail.

To achieve faster processing, FastMOT only runs the detector and feature extractor every N frames, while KLT fills in the gaps efficiently. FastMOT also re-identifies objects that moved out of frame and will keep the same IDs.

YOLOv4 was trained on CrowdHuman (82% mAP@0.5) while SSD's are pretrained COCO models from TensorFlow. Both detection and feature extraction use the TensorRT backend and perform asynchronous inference. In addition, most algorithms, including KLT, Kalman filter, and data association, are optimized using Numba.

Performance

Results on MOT20 train set

Detector Skip	MOTA	IDF1	HOTA	MOTP	MT	ML
N = 1	66.8%	56.4%	45.0%	79.3%	912	274
N = 5	65.1%	57.1%	44.3%	77.9%	860	317

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 TrackEval. 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. Increasing N shows small impact on MOTA. Tracking speed can reach up to 38 FPS depending on the number of objects. Lighter models (e.g. YOLOv4-tiny) are recommended for a more constrained device like Jetson Nano. FPS is expected to be in the range of 50 - 150 on desktop CPU/GPU.

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 x86 Ubuntu

Make sure to have nvidia-docker installed. The image requires an NVIDIA Driver version >= 450 for Ubuntu 18.04 and >= 465.19.01 for Ubuntu 20.04. Build and run the docker image:

# For Ubuntu 20.04, add --build-arg TRT_IMAGE_VERSION=21.05
docker build -t fastmot:latest .

# Run xhost + first if you have issues with display
docker run --gpus all --rm -it -v $(pwd):/usr/src/app/FastMOT -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY -e TZ=$(cat /etc/timezone) 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

cd fastmot/plugins
make

Download VOC dataset for INT8 calibration

Only required for SSD (not supported on Ubuntu 20.04)

./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

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

Change class labels here to your object classes
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

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')

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 Deep SORT and KLT}},
 month        = nov,
 year         = 2020,
 publisher    = {Zenodo},
 version      = {v1.0.0},
 doi          = {10.5281/zenodo.4294717},
 url          = {https://doi.org/10.5281/zenodo.4294717}
}

TheMemoryDealer/FastMOT