This repository is the implementation of the paper "OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment", IEEE Transactions on Intelligent Transportation Systems.

We leveraged the power of 3D graphics and computer vision techniques to tackle a real-world problem, that we propose object-to-spot rotation estimation which is of particular significance for intelligent surveillance systems, bike-sharing systems, and smart cities. We introduced a rotation estimator (OSRE) that estimates a parked bike rotation with respect to its parking area. By leveraging 3D graphics, we generated Synthetic Bike Rotation Dataset (SynthBRSet) with accurate bike rotation annotations. Next, we presented a first-of-its-type object rotation model that assesses bike parking. As we obtained promising results, we believe this work would be a significant starting point for further deep studies on rotation estimation with respect to other visual semantic elements in the input image.

This repository is based on the YOLOv7 structure and assumes that all dependency for training YOLOv7 is already installed.

Object-to-Spot Rotation Estimation. Our proposed object-to-spot rotation estimation method provides two rotation angle predictions in two axes $y$ and $z$. Rotation in $y$ axis represents the bicycle leaning/falling left or right. When it is fallen down to the left side, it represents the angle $90^\circ$ or $\pi/2$ in radians, whereas falling down to the right size is representing the $270^\circ$ or $3\pi /2$. Similarly, rotation in $z$ axis represents the bike direction in its standing pose. i.e., (C) shows the rotated bike in $z$ axis ( rotated class) in blue color, the bikes lean left/right ( fallen class) are shown in orange color, and the well-parked bikes ( parked class) are plotted in green color. We visualize $z$ rotation in the inner circle and $y$ is visualized in the outer circle.

Rotation Visualization. We represent rotation in two axes, the outer angle represents the rotation of the object (e.g., bike) in $y$ axis, whereas the inner angle represents the object rotation in $z$ axis. We assume the bike well parked is not rotated in either axes. Rotating the bike in $y$ axis leads to leaning the bike and/or fall it down in the ground. The rotated bike in $y$ axis may and may not be associated with rotation in $z$ axis at the same time. However, bikes can be rotated only in $z$ axis which indicates that it is standing and parked but in an inaccurate direction.

The proposed OSRE was trained on Synthetic Bike Rotation Dataset (SynthBRSet) dataset. Follow the instructions in SynthBRSet to generate the dataset. The generated dataset will be in Yolo annotation formate. It involves the rotation annotation of each bike. The generating code creates COCO annotation as well. Synthetic Bike Rotation Dataset (SynthBRSet) contains a wide variety in lighting, objects' colors, objects' poses, camera view, and scenes.

Overall, the dataset includes the images and annotations directories. It is expected to have the generated synthetic dataset in the following dirctory structure:

SynthBRSet
│   images
│       └─000001.jpg
│       └─000002.jpg
│       └─000003.jpg
|   labels
│       └─000001.txt
│       └─000002.txt
│       └─000003.txt

Each line of each annotation file involves the rotation annotation of the bicycle in two axes $y$ and $z$. Rotations are read in radian, then normalization into [0-1].

Example the annotation file 006444.txt:

cls    x        y        w        h        θy       θz
 1   0.533666 0.539923 0.028769 0.156479 0.500000 0.482248

Train a suitable model by running the following command using a pretrained Yolov7 CKPTs. This provides better performance than training the model from scratch. To train the model OSRE-yolov7-IDetect in a single GPU with Yolov7 baseline yolov7-IDetect, input resolution 640 x 640, and a weighted rotation loss 0.1, first, download yolov7.pt.

python train.py  --batch-size 16 --epochs 100 --data data/OSRE_dataRotation.yaml --img 640 640 --cfg cfg/training/OSRE-yolov7-IDetect.yaml --name Exp_OSRE_yolov7_Rotation_loss_0_1 --weights weights/yolov7.pt --hyp data/hyp.scratch.Rotation_loss_01.yaml 

To train the same model in a distributed multi-GPU mode, e.g., 8 GPUs:

python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train.py  --workers 8  --batch-size 64 --epochs 100 --data data/OSRE_dataRotation.yaml --img 640 640 --cfg cfg/training/OSRE-yolov7-IDetect.yaml --name Exp_OSRE_yolov7_Rotation_loss_0_1 --weights weights/yolov7.pt --hyp data/hyp.scratch.Rotation_loss_01.yaml

To test a trained model (e.g., OSRE-yolov7-IDetect) on a synthetic or real testing set (The testing set path is specified in data/OSRE_dataRotation.yaml ):

python testRotation.py --data data/OSRE_dataRotation.yaml --cfg cfg/training/OSRE-yolov7-IDetect.yaml --img 640 --batch 1 --conf 0.001 --iou 0.65 --device 0 --weights weights/osre.pt --name OSRE_yolov7_640_val

python detectRotation.py  --img-size 640 --conf 0.3 --iou 0.65 --device 0 --weights osre.pt --source inference/videos_smoothed_with_2D_convolution

Performance of the proposed OSRE on top of several object detection baselines. The models are trained on half SynthBPSet excluding the challenging set and evaluated on SynthBPSet and the real sets.

• Here are samples of the proposed method performance on synthetic image (in the left) and on real collected image (in the right).

Performance on Synthetic Image	Performance on Real Image

@article{alfasly2023OSRE,
  author={Alfasly, Saghir and Al-Huda, Zaid and Bello, Saifullahi Aminu and Elazab, Ahmed and Lu, Jian and Xu, Chen},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment}, 
  year={2023},
  volume={},
  number={},
  pages={1-10},
  doi={10.1109/TITS.2023.3330786}}

The code is built upon YOLOV7