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STCMOT

[ICME 2024] STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking.


>One-shot multi-class multi-object tracking for UAV videos

🚩 Abstract

Mobject tracking (MOT) in Unmanned Aerial Vehicle (UAV) videos is important for diverse applications in computer vision. Current MOT trackers rely on accurate object detection results and precise matching of target re-identification (ReID). These methods focus on optimizing target spatial attributes while overlooking temporal cues in modelling object relationships, especially for challenging tracking conditions such as object deformation and blurring, etc. To address the above-mentioned issues, we propose a novel Spatio-Temporal Cohesion Multiple Object Tracking framework (STCMOT), which utilizes historical embedding features to model the representation of ReID and detection features in a sequential order. Concretely, a temporal embedding boosting module is introduced to enhance the discriminability of individual embedding based on adjacent frame cooperation. While the trajectory embedding is then propagated by a temporal detection refinement module to mine salient target locations in the temporal field. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate our STCMOT sets a new state-of-the-art performance in MOTA and IDF1 metrics.

🗼 Pipeline of STCMOT

💁 Get Started

Environment preparation

git clone https://github.com/ydhcg-BoBo/STCMOT.git
conda create -n STCMOT
conda activate STCMOT
# Note: GPU 3090 for cuda 11.0
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.htm
cd ${STCMOT_ROOT}
pip install cython
pip install -r requirements.txt
git clone -b pytorch_1.7 https://github.com/ifzhang/DCNv2.git
cd DCNv2
./make.sh

Data preparation

Train

sh experiments/train_stcmot.sh

Test

cd src
Run python track.py

The multi-object tracking results will be saved as **.txt, and evaluate it by the official toolkits

Acknowledgement

A large part of the code is borrowed from Fairmot and MCMOT. Thanks for their wonderful works.

Connection

If you have any questions related to the paper and the code please contact me

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