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A complete implementation of paper Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos is

1. Fine-tune the vehicle detector with the dataset UAV-Vehicle-Detection-Dataset.

2. Step by step fine-tuning the vehicle detector Fine-tune-YOLOv3.

3. A multi-vehicle tracking is conducted by deep_sort_yolov3.

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This is a customized tracker implementation of paper Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos. The tracker is modified based on cosine_metric_learning and deep_sort_yolov3.

For multi-vehicle detection, refer to UAV-Vehicle-Detection-Dataset.

Key features added compared to deep_sort_yolov3:

1. The FPS of the output tracking videos is exactly the same as the input videos.
2. A .txt file (tracking_DJI_0006.txt) contains the tracked vehicle locations in the pixel frame is generated.

1. Train the deep association metric model

1. Clone cosine_metric_learning feature/veri_dataset branch

git clone --branch feature/veri_dataset https://github.com/nwojke/cosine_metric_learning.git

2. Obtain the VeRi dataset
3. Training on Veri dataset

python3 train_veri.py --dataset_dir=./VeRi_with_plate --loss_mode=cosine-softmax --log_dir=./output/veri/ --run_id=cosine-softmax

4. Monitoring training process

tensorboard --logdir ./output/veri/sine-softmax --port 6006

5. Obtain the trained weights

python3 train_veri.py --mode=freeze --restore_path=output/veri/cosine-softmax/model.ckpt-334551

This will create a veri.pb file which can be supplied to Deep SORT. Again, the Market1501 script contains a similar function.

2. Track multiple vehicles using deep sort package

1. Download yolov3_dji_final.weights from UAV-Vehicle-Detection-Dataset. Convert the yolo weight to a keras model by

python3 convert.py yolov3_dji.cfg yolov3_dji_final.weights model_data/yolo_dji.h5

OR, download my converted one at yolo_dji.h5

2. Clone this repository

git clone https://github.com/jwangjie/deep_sort_yolov3

3. Run the multi-vehicle tracking

python tracker.py