CaMuViD: Calibration-Free Multi-View Detection

The official implementation of CaMuViD: Calibration-Free Multi-View Detection.

Introduction

CaMuViD is an advanced multi-view pedestrian detection model developed by researchers from AI Laboratory, University of Florida. The backbone of this model is based on InternImage, InternImage employs DCNv3 as its core operator. This approach equips the model with dynamic and effective projection and back projection estimation networks to transform from image space feature maps to shared space feature maps for multi-views.

🌟 Performance Highlights

• State-of-the-Art Multi-View Detection
  On Wildtrack we achieve 95.0 % MODA (+0.9 pp over prior best) and 80.9 % MODP, with 96.3 % precision, 98.6 % recall, and 97.4 % F₁.
  On MultiviewX we reach 96.5 % MODA, 89.3 % MODP, 97.9 % precision, 98.6 % recall, and 98.3 % F₁—all best-in-class.

• Exceptional Cross-Dataset Generalization
  Trained on MultiviewX and tested on Wildtrack, our model still delivers 86.4 % MODA, 60.7 % MODP, 89.3 % precision, 98.1 % recall, and 93.5 % F₁, outperforming prior methods by > 9 pp in F₁.

• Ablation: The Power of Multiple Views
  Removing views drops performance dramatically—single-camera yields only 60.1 % MODA and 65.0 % MODP.
  By integrating five cameras, we recover to 95.6 % MODA, 80.2 % MODP, 96.6 % precision, 99.3 % recall, and 98.0 % F₁, underscoring the indispensable value of comprehensive multi-view coverage.

Multi-View Detection Results

Method	WT MODA ↑	WT MODP ↑	WT Prec. ↑	WT Rec. ↑	WT F1 ↑	MVX MODA ↑	MVX MODP ↑	MVX Prec. ↑	MVX Rec. ↑	MVX F1 ↑
RCNN & clustering	11.3	18.4	68.0	43.0	52.7	18.7*	46.4*	63.5*	43.9*	51.9
POM-CNN	23.2	30.5	75.0	55.0	63.5	–	–	–	–	–
DeepMCD	67.8	64.2	85.0	82.0	83.5	70.0*	73.0*	85.7*	83.3*	84.5
Deep-Occlusion	74.1	53.8	95.0	80.0	86.8	75.2*	54.7*	97.8*	80.2*	88.1
MVDet	88.2	75.7	94.7	93.6	94.1	83.9	79.6	96.8	86.7	91.5
SHOT	90.2	76.5	96.1	94.0	95.0	88.3	82.0	96.6	91.5	94.0
MVDeTr	91.5	82.1	97.4	94.0	95.7	93.7	91.3	99.5	94.5	96.9
MVAug	93.2	79.8	96.3	97.0	96.6	95.3	89.7	99.4	95.9	97.6
3DROM	93.5	75.9	97.2	96.2	96.7	95.0	84.9	99.0	96.1	97.5
MVFP	94.1	78.8	96.4	97.7	97.0	95.7	82.1	98.4	97.2	97.8
TrackTacular	93.2	77.5	97.3	95.8	96.5	96.5	75.0	99.4	97.1	98.2
Ours	95.0	80.9	96.3	98.6	97.4	96.5	89.3	97.9	98.6	98.3

* reported by original authors

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Cross-Dataset Performance

Method	MODA	MODP	Prec.	Recall	F1
MVDet [17]	17.0	65.8	60.5	48.8	54.0
MVAug [10]	26.3	58.0	71.9	50.8	59.5
MVDeTr [16]	50.2	69.1	74.0	77.3	75.6
SHOT [34]	53.6	72.0	75.2	79.8	77.4
GMVD [39]	66.1	72.2	82.0	84.7	83.3
3DROM [30]	67.5	65.6	94.5	71.7	81.5
MVFP [1]	76.7	74.9	85.2	92.8	88.8
Ours	86.4	60.7	89.3	98.1	93.5

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Camera-Elimination Ablation

C1	C2	C3	C4	C5	C6	C7	MODA	MODP	Precision	Recall	F1
✓	✗	✗	✗	✗	✗	✗	60.1	65.0	99.8	60.2	75.1
✓	✓	✗	✗	✗	✗	✗	77.8	71.5	99.8	77.9	87.5
✓	✓	✓	✗	✗	✗	✗	90.6	76.4	98.4	92.1	95.1
✓	✓	✓	✓	✗	✗	✗	93.8	79.4	98.3	95.5	96.9
✓	✓	✓	✓	✓	✗	✗	93.8	80.0	96.8	96.9	97.0
✓	✓	✓	✓	✓	✓	✗	95.6	80.2	96.6	99.3	98.0
✓	✓	✓	✓	✓	✓	✓	95.0	80.9	96.3	98.6	97.4

Our detection code is developed on top of MMDetection v2.28.1.

• Installation
• Data Preparation
• Evaluation
• Training

Installation

• Clone this repository:

git clone https://github.com/amiretefaghi/CaMuViD.git
cd CaMuViD

• Create a conda virtual environment and activate it:

conda create -n camuvid python=3.9
conda activate camuvid

• Install CUDA>=10.2 with cudnn>=7 following the official installation instructions
• Install PyTorch>=1.10.0 and torchvision>=0.9.0 with CUDA>=10.2:

For examples, to install torch==1.11 with CUDA==11.3:

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html

• Install other requirements:

  note: conda opencv will break torchvision as not to support GPU, so we need to install opencv using pip.

conda install -c conda-forge termcolor yacs pyyaml scipy pip -y
pip install opencv-python

• Install timm, mmcv-full and `mmsegmentation':

pip install -U openmim
mim install mmcv-full==1.5.0
mim install mmsegmentation==0.27.0
pip install timm==0.6.11 mmdet==2.28.1

• Install other requirements:

pip install opencv-python termcolor yacs pyyaml scipy
# Please use a version of numpy lower than 2.0
pip install numpy==1.26.4
pip install pydantic==1.10.13
pip install yapf==0.40.1

• Compile CUDA operators

Before compiling, please use the nvcc -V command to check whether your nvcc version matches the CUDA version of PyTorch.

cd ./ops_dcnv3
sh ./make.sh
# unit test (should see all checking is True)
python test.py

• You can also install the operator using precompiled .whl files DCNv3-1.0-whl

Data Preparation

Prepare datasets according to the guidelines in MMDetection v2.28.1.

For your convenient, we provided the COCO format annotations of MultiviewX and Wildtrack in the ./data folder.

Evaluation

To evaluate our model on desired dataset, set mode to test in the Config.py file, set the num_head and num_views to 7 for Wildtrack and 6 for MultiviewX, run:

python camuvid.py

The model weights for MultiviewX and Wildtrack are available.

Training

To finetune an InternImage backbone on desired dataset, run:

python train.py ./configs/{dataset_name}/mask_rcnn_internimage_b_fpn_3x_coco.py  --load-from ./models/coco_pretrained/mask_rcnn_internimage_b_fpn_3x_coco.pth

and then train the detection section, you should modify the Config.py file. You should set the mode to train and write the address of train and test annotations directory, then run:

python camuvid.py

Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@InProceedings{etefaghi2025camuvid,
                            author    = {Daryani, Amir Etefaghi and Bhutta, M. Usman Maqbool and Hernandez, Byron and Medeiros, Henry},
                            title     = {CaMuViD: Calibration-Free Multi-View Detection},
                            booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
                            month     = {June},
                            year      = {2025},
                            pages     = {1220-1229}
                        }