Modern C++ Multi-Object Tracking — 10 SOTA algorithms, production-ready, 10–100× faster than Python

Features · Benchmarks · Installation · Quick Start · Trackers · Documentation · Citation

ByteTrack — 1100 FPS	OC-SORT — 850 FPS
BoostTrack — HOTA 67.5	SORT — 1250 FPS

motcpp is a high-performance C++ library for multi-object tracking (MOT). It implements 10 state-of-the-art algorithms with a unified, modern C++17 API — covering everything from the lightweight SORT baseline to the current BoostTrack SOTA — all ready to drop into production.

Inspired by BoxMOT, motcpp brings the same algorithmic breadth to C++ with a clean CMake integration, ONNX Runtime ReID backend, and built-in MOT benchmark tooling.

• 10 SOTA trackers — SORT, ByteTrack, OC-SORT, DeepOC-SORT, StrongSORT, BoT-SORT, BoostTrack, HybridSORT, UCMCTrack, OracleTrack
• 10–100× faster than Python — optimized C++ hot paths, zero-copy Eigen matrices
• Unified API — one update(dets, img) call across all trackers
• ONNX ReID backend — plug in any appearance model as an .onnx file
• Camera motion compensation — ORB/ECC/SoF CMC built-in
• MOT benchmark tooling — evaluate on MOT17/MOT20 out of the box
• >90% test coverage — GoogleTest suite across all components
• Cross-platform — Linux, macOS, Windows (CI-verified)
• Modern CMake — find_package, FetchContent, and vcpkg ready

Evaluated on the second half of the MOT17 training set using YOLOX detections and FastReID embeddings. Pre-generated data available in releases. FPS on Intel i9-13900K, single thread.

sudo apt-get update && sudo apt-get install -y \
    build-essential cmake \
    libeigen3-dev libopencv-dev libyaml-cpp-dev

brew install cmake eigen opencv yaml-cpp

vcpkg install eigen3:x64-windows opencv4:x64-windows yaml-cpp:x64-windows

git clone https://github.com/Geekgineer/motcpp.git
cd motcpp
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc)
sudo cmake --install build

find_package(motcpp REQUIRED)
target_link_libraries(your_target PRIVATE motcpp::motcpp)

include(FetchContent)
FetchContent_Declare(
    motcpp
    GIT_REPOSITORY https://github.com/Geekgineer/motcpp.git
    GIT_TAG        v1.0.0
)
FetchContent_MakeAvailable(motcpp)
target_link_libraries(your_target PRIVATE motcpp::motcpp)

#include <motcpp/trackers/bytetrack.hpp>
#include <opencv2/opencv.hpp>

int main() {
    motcpp::trackers::ByteTrack tracker;

    cv::VideoCapture cap("video.mp4");
    cv::Mat frame;

    while (cap.read(frame)) {
        // Detector output: [x1, y1, x2, y2, confidence, class_id]
        Eigen::MatrixXf dets = your_detector(frame);

        // Update — returns [x1, y1, x2, y2, track_id, conf, class_id, det_idx]
        Eigen::MatrixXf tracks = tracker.update(dets, frame);

        for (int i = 0; i < tracks.rows(); ++i) {
            int id = static_cast<int>(tracks(i, 4));
            cv::Rect box(tracks(i, 0), tracks(i, 1),
                         tracks(i, 2) - tracks(i, 0),
                         tracks(i, 3) - tracks(i, 1));
            cv::rectangle(frame, box, motcpp::BaseTracker::id_to_color(id), 2);
            cv::putText(frame, "ID " + std::to_string(id),
                        box.tl(), cv::FONT_HERSHEY_SIMPLEX, 0.6,
                        motcpp::BaseTracker::id_to_color(id), 2);
        }

        cv::imshow("Tracking", frame);
        if (cv::waitKey(1) == 27) break;
    }
}

#include <motcpp/trackers/boosttrack.hpp>

// Point to a ReID ONNX model (see docs/guides/trackers.md for download)
motcpp::trackers::BoostTrackTracker tracker("osnet_x1_0.onnx");

while (cap.read(frame)) {
    Eigen::MatrixXf dets  = detector(frame);
    Eigen::MatrixXf tracks = tracker.update(dets, frame);
    // ...
}

motcpp::trackers::ByteTrack tracker(
    0.3f,   // det_thresh
    30,     // max_age
    50,     // max_obs
    3,      // min_hits
    0.3f,   // iou_threshold
    true,   // per_class — track each class independently
    80      // nr_classes
);

tracker.reset();   // clears all track state and ID counter

Need maximum throughput (>500 FPS)?
    └─ SORT or ByteTrack

General purpose, great accuracy/speed balance?
    └─ ByteTrack · OC-SORT · OracleTrack

Heavy occlusion or non-linear motion?
    └─ OC-SORT · UCMCTrack · OracleTrack

Moving or drone camera?
    └─ UCMCTrack · BoT-SORT · OracleTrack

Have a ReID model and need best accuracy?
    └─ BoostTrack · StrongSORT · BoT-SORT

Full parameter reference and per-tracker code snippets: docs/guides/trackers.md

# Download OSNet x1.0 (recommended — good accuracy/size balance)
./scripts/auto_benchmark.sh --download-reid

# Or directly:
wget https://github.com/Geekgineer/motcpp/releases/download/reid-models-v1.0.0/osnet_x1_0.onnx

cmake -B build -DMOTCPP_BUILD_TESTS=ON
cmake --build build -j$(nproc)
cd build && ctest --output-on-failure

# Single test suite
./build/tests/motcpp_tests --gtest_filter=ByteTrackTest.*

Contributions are welcome — bug fixes, new trackers, documentation, benchmarks. See CONTRIBUTING.md for the workflow.

If you use motcpp in your research, please cite:

@software{motcpp2026,
  author  = {motcpp contributors},
  title   = {motcpp: Modern C++ Multi-Object Tracking Library},
  year    = {2026},
  url     = {https://github.com/Geekgineer/motcpp},
  license = {AGPL-3.0}
}

Please also cite the original algorithm papers. See Acknowledgments below.

Licensed under the GNU Affero General Public License v3.0. See LICENSE.

motcpp follows the architecture and algorithmic patterns of BoxMOT by Mikel Broström — the Python multi-object tracking library that inspired this C++ port.

Made with ❤️ by the motcpp community