{"id":15132094,"url":"https://github.com/vishal-038/real_time_object_detection","last_synced_at":"2026-02-02T14:32:41.234Z","repository":{"id":255514328,"uuid":"852306451","full_name":"VISHAL-038/Real_Time_Object_Detection","owner":"VISHAL-038","description":" Object Detection in Real-Time Video Stream This project demonstrates the use of YOLOv3 (You Only Look Once version 3) for real-time object detection in video streams. YOLOv3 is a state-of-the-art deep learning model known for its balance between speed and accuracy in detecting objects within images and videos. 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YOLOv3 is a state-of-the-art deep learning model known for its balance between speed and accuracy in detecting objects within images and videos.\n\nKey Features\nReal-Time Detection: Processes video frames quickly enough for real-time applications.\nMulti-Class Detection: Detects multiple objects from a predefined set of classes (e.g., people, vehicles, animals).\nHigh Accuracy: Provides accurate object localization and classification in each frame.\nHow It Works\nLoad the Model: The YOLOv3 model is loaded using pre-trained weights and configuration files. These files define the architecture and parameters of the model.\n\nPrepare Video Stream: Captures frames from a video source (e.g., webcam, video file) for processing.\n\nPreprocess the Frame: Each frame is resized and normalized to fit the input requirements of the YOLOv3 model.\n\nRun Detection: The preprocessed frame is passed through the YOLOv3 network to obtain bounding boxes, class labels, and confidence scores for detected objects.\n\nPostprocess and Display: Detected objects are highlighted with bounding boxes and labels, and the processed frame is displayed in a window to show real-time results.\n\nHandle Output: Continuously processes video frames and updates the display until the user stops the stream.\n\nRequirements\nPython: Programming language used for the project.\nOpenCV: Library for video capture, image processing, and display.\nYOLOv3 Model Files:\nYOLOv3 Weights\nYOLOv3 Config\nCOCO Names\nThis project demonstrates how YOLOv3 can be integrated into real-time systems for various applications, such as surveillance, autonomous vehicles, and interactive video analysis.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishal-038%2Freal_time_object_detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvishal-038%2Freal_time_object_detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishal-038%2Freal_time_object_detection/lists"}