Awesome GitHub RepositoriesObject Tracking

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

Supervision is a computer vision toolset for normalizing model outputs, managing datasets, and visualizing annotations. It provides a framework to convert predictions from various classification and detection models into a standardized data format to ensure interoperability across different computer vision pipelines. The library features a post-processor for filtering, counting, and tracking detected objects across image frames and video streams. It includes capabilities for large image tiling to improve the detection of small objects and tools for assigning persistent identities to objects t

Frigate is a self-hosted network video recorder that functions as a private, local AI-powered vision engine. It manages video streams by performing real-time object detection, tracking, and classification directly on local hardware, ensuring that security monitoring and activity recording remain independent of cloud services. The system distinguishes itself through a modular, hardware-accelerated video pipeline that offloads intensive decoding and machine learning inference to dedicated GPUs, NPUs, or specialized accelerators like Coral TPUs and Hailo modules. It utilizes state-based object t

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a complete pipeline for identifying and localizing objects in images and video using a single neural network pass, combining a Darknet-53 backbone with multi-scale feature pyramids and anchor-based bounding box prediction. The framework extends beyond basic detection to include instance segmentation, human pose estimation, and multi-object tracking across video frames. It offers a model export toolkit that converts trained models through ONNX to CoreML, TensorFlow Lite, and Ten

tracking.js is a browser computer vision library written in JavaScript for performing real-time image analysis and object tracking directly within a web browser. It functions as a real-time object tracker, a color tracking tool, and a face detection utility. The library enables the detection and monitoring of specific color ranges, human faces, and known visual patterns across consecutive video frames. It extracts visual features and descriptors from images to identify distinct landmarks for matching and tracking. The project covers broad computer vision capabilities, including the ability t

Boxmot is a multi-object tracking framework designed to follow multiple objects across video frames using motion and appearance algorithms to maintain consistent identities. It functions as a system for tracking objects with specific orientations using rotated bounding boxes and corresponding intersection-over-union computations. The project includes a re-identification model optimizer that converts neural networks into formats for hardware-accelerated execution. It also features an evolutionary hyperparameter tuner that iteratively mutates tracker settings to maximize accuracy for specific d

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

ccv is a computer vision library written in C designed for high-performance visual analysis. It serves as a framework for image classification, object detection, and the identification of faces, pedestrians, and vehicles. The library distinguishes itself through hardware-accelerated vision and deep learning inference optimizations. It utilizes a quantized tensor processor to transform floating-point data into eight-bit integers and implements integer-quantized attention mechanisms to reduce memory bandwidth and increase data throughput. The project covers a broad range of capabilities, inclu

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

ByteTrack is a multi-object tracking framework that implements the ByteTrack algorithm, an ECCV 2022 method designed to recover occluded objects and reduce trajectory fragmentation. The core innovation of the project is its association algorithm, which processes every detection box—including low-confidence ones—by using separate high and low score thresholds, Kalman filter motion prediction, and Hungarian algorithm matching to produce consistent object identities across video frames. The project distinguishes itself by its comprehensive approach to handling occlusions and fragmented trajector

This is an open-source autonomous driving perception pipeline that processes camera and lidar sensor data to detect, track, and fuse objects in real-world driving environments. The project integrates an end-to-end perception workflow combining sensor calibration, deep learning object detection, Kalman filter tracking, and sensor fusion for robust scene understanding. The pipeline includes camera calibration tools to remove lens distortion from raw images, deep learning model training for object classification and detection, and multi-object tracking using Kalman filters with data association

DeepSORT ist ein Framework für Echtzeit-Multi-Objekt-Tracking, das darauf ausgelegt ist, konsistente Identitäten mehrerer Objekte über Video-Frames hinweg beizubehalten. Es integriert Deep-Learning-Erscheinungsmerkmale mit Bewegungsdeskriptoren, um Objekte durch eine Sequenz von Videodaten zu verfolgen. Das System nutzt ein tiefes Convolutional Neural Network, um hochdimensionale visuelle Deskriptoren für die Personen-Re-Identifizierung zu generieren. Diese Erscheinungsmerkmale werden mit Bewegungsschätzung via Kalman-Filter kombiniert und mittels des ungarischen Algorithmus gelöst, um Detektionen optimal mit bestehenden Tracks zu assoziieren. Das Framework enthält Funktionen für Gating-basiertes Assoziations-Filtering und Zustands-basiertes Track-Management, um Objekt-Lifecycles zu handhaben. Zudem bietet es Tools zum Rendern von Tracking-Ergebnissen auf Video-Frames und zur Evaluierung der Tracking-Leistung anhand etablierter Benchmarks.

Navigation2 is a ROS 2 navigation framework for autonomous mobile robots. It provides the core identity of a path planner, costmap management system, kinematic motion controller, and behavior tree orchestrator to compute collision-free routes and execute movement commands. The framework is distinguished by its use of behavior trees to coordinate modular task servers, enabling complex navigation routines and autonomous recovery actions. It supports a plugin-based architecture that allows planners and controllers to be swapped at runtime to adapt to different environments. The system covers a

Dieses Projekt ist ein Framework für Multi-Objekt-Tracking, das entwickelt wurde, um erkannten Bounding-Boxen über aufeinanderfolgende Videobilder hinweg dauerhafte Identitäten zuzuweisen. Es fungiert als Computer-Vision-Tracking-Algorithmus, der mehrere sich bewegende Ziele in Echtzeit überwacht, indem Erkennungen mit konsistenten Labels verknüpft werden. Das System nutzt einen Schätzansatz für den Zustand, der auf einem Kalman-Filter basiert, um zukünftige Objektpositionen vorherzusagen und die Identität bei Erkennungslücken aufrechtzuerhalten. Es verwendet den ungarischen Algorithmus für eine optimale Datenzuordnung und berechnet die Intersection over Union, um vorhergesagte Track-Positionen mit tatsächlichen Erkennungen abzugleichen. Die Verarbeitungspipeline verwaltet ein Register aktiver Tracks unter Verwendung eines linearen Modells konstanter Geschwindigkeit, um Zustandsübergänge zu vereinfachen. Sie führt eine rekursive Bild-für-Bild-Verarbeitung durch, um den Zustand aller verfolgten Objekte zu aktualisieren, während neue Bilder analysiert werden.

FairMOT is a multi-object tracking framework and deep learning model designed to identify and track multiple entities across video frames. It implements a unified pipeline that integrates object detection and identity re-identification into a single-stage joint network. The system utilizes an anchor-free detection method to predict object centers and bounding box dimensions. It maintains identity consistency across consecutive frames by generating high-dimensional embedding vectors for re-identification and employing a Kalman filter for motion state prediction. The framework covers a broad r

This project is a multi-object tracking library and computer vision toolkit designed to maintain consistent identity IDs for objects across video frames. It provides a motion-based object tracking system that converts raw detections into stable temporal tracks, enabling the analysis of object movement and behavior over time. The toolkit distinguishes itself through advanced identity maintenance, utilizing Kalman filters for linear motion tracking and sparse optical flow for camera motion estimation. It features multi-stage object association to recover occluded objects and non-linear motion t

Dieses Projekt ist ein Computer-Vision-System, das für Gesichtserkennung und Identitätsverfolgung in Echtzeit mittels Live-Kamera-Feeds entwickelt wurde. Es bietet ein Framework zum Erfassen, Registrieren und Identifizieren mehrerer Personen gleichzeitig, indem Live-Video-Input mit einer lokalen Datenbank vorregistrierter Gesichtsbeschreibungen verglichen wird. Das System zeichnet sich durch eine leistungsorientierte Verarbeitungspipeline aus, die die Rechenlast während der Live-Analyse ausbalanciert. Durch die Kombination von Feature-Extraktion mittels tiefer neuronaler Netze mit zentroidbasierter Objektverfolgung behält die Software konsistente Identitäts-Labels über Videoframes hinweg bei und minimiert gleichzeitig die Häufigkeit rechenintensiver Erkennungsvorgänge. Dieser Ansatz ermöglicht eine stabile Verfolgung und Identifizierung mehrerer Personen, ohne dass eine vollständige Verarbeitung für jeden Frame erforderlich ist. Die Bibliothek unterstützt eine Reihe von Identitätsverwaltungsaufgaben, einschließlich der Erstellung durchsuchbarer Gesichtsdatenbanken und der automatisierten Protokollierung von Personen. Sie handhabt den gesamten Lebenszyklus biometrischer Daten, von der anfänglichen Extraktion eindeutiger numerischer Vektoren aus Kamerabildern bis zur persistenten Speicherung dieser Beschreibungen auf dem lokalen Dateisystem für zukünftige Verifizierungen.

This project is a computer vision pipeline that integrates object detection and tracking to monitor moving objects within video streams. It functions as an end-to-end analytics tool that processes video frames to identify, classify, and maintain the unique identity of objects as they move through a scene. The system utilizes a combination of deep learning inference for detection and motion estimation to ensure temporal continuity. By pairing visual appearance descriptors with predictive motion modeling, it maintains object identities even during temporary occlusions or when spatial overlap is

This project is a computer vision framework designed for the detection, identification, and tracking of human subjects within video streams. It provides an integrated system for locating individuals, generating biometric models from image datasets, and maintaining identity labels across consecutive video frames. The system distinguishes itself through its ability to maintain identity persistence across multiple camera feeds. By utilizing deep learning inference to extract feature vector embeddings and applying motion prediction algorithms, it links unique identity signatures across disparate