Robust Associations Multi-Pedestrian Tracking using motion and appearance information with camera-motion compensation.

BoT-SORT is a state-of-the-art multi-object tracker designed for detecting and tracking objects in a scene, maintaining unique identifiers for each. It combines motion and appearance information with camera-motion compensation and an accurate Kalman filter. BoT-SORT and BoT-SORT-ReID trackers excel in MOTChallenge datasets, achieving top ranks in MOTA, IDF1, and HOTA metrics. It leverages YOLOX and YOLOv7 for object detection and supports multi-class tracking. The architecture facilitates camera motion compensation using OpenCV's VideoStab Global Motion Estimation. Installation involves setting up a Conda environment, installing PyTorch, and using pip to install necessary packages and dependencies like ByteTrack and FastReID.
BoT-SORT is a state-of-the-art multi-object tracker designed for detecting and tracking objects in a scene, maintaining unique identifiers for each.
Explore all tools that specialize in maintaining unique object ids. This domain focus ensures BoT-SORT delivers optimized results for this specific requirement.
Explore all tools that specialize in camera motion compensation. This domain focus ensures BoT-SORT delivers optimized results for this specific requirement.
Explore all tools that specialize in multi-class object identification. This domain focus ensures BoT-SORT delivers optimized results for this specific requirement.
Compensates for camera movements to improve tracking accuracy. It's based on OpenCV's VideoStab Global Motion Estimation.
Maintains the identity of objects even after temporary occlusions using appearance features extracted by a ReID model.
Uses a more accurate Kalman filter state vector to predict the future positions of tracked objects.
Integrates with state-of-the-art object detection models, offering flexibility and high detection accuracy.
Tracks multiple object categories simultaneously using object detectors trained on diverse datasets such as COCO.
Create a Conda environment: `conda create -n botsort_env python=3.7`
Activate the environment: `conda activate botsort_env`
Install PyTorch and torchvision: Use the appropriate command from pytorch.org for your CUDA version.
Clone the BoT-SORT repository: `git clone https://github.com/NirAharon/BoT-SORT.git`
Navigate to the BoT-SORT directory: `cd BoT-SORT`
Install the required Python packages: `pip3 install -r requirements.txt`
Install the package in develop mode: `python3 setup.py develop`
Install pycocotools: `pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`
(Optional) Install Cython-bbox: `pip3 install cython_bbox`
(Optional) Install FAISS (CPU or GPU): `pip3 install faiss-cpu` or `pip3 install faiss-gpu`
All Set
Ready to go
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"Highly accurate and robust tracker, particularly effective in crowded scenes with camera motion."
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