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Real Time Object Tracking with DeepSORT and YOLOv8 in Google Colab

Real-time object detection and multi-object tracking using YOLOv8 and DeepSORT. Includes edge optimization with TensorFlow Lite, deployment ready with Docker containerization.

The YOLOv8 model is used for real-time object detection in car image detection, identifying cars on images and videos. It includes preprocessing, object detection, object tracking with DeepSORT, and vehicle counting. This technology could increase traffic efficiency and improve safety measures.

Hybrid Decision Engine for real-time threat detection. Combines YOLOv8/ONNX object detection, DeepSort tracking, and 5-level state management (NORMAL→CRITICAL). Features temporal analysis, Bayesian fusion, and MCDM scoring. Production-ready with 1000+ FPS performance, comprehensive testing, and seamless integration for smart security systems.

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Real-time object detection and tracking with YOLOv8, DeepSORT, and Streamlit.

Real-time multi-object tracking with YOLOv8, Kalman Filter, and ReID (DeepSORT-like system)

Real-time object detection and multi-object tracking system using YOLOv8 and DeepSORT with FastAPI and Streamlit dashboard

Real-Time Vehicle & Object Detection with YOLOv8 This project implements a real-time object detection, tracking, and counting system based on YOLOv8 and DeepSORT (ReID).

Real-time Object Detection and Tracking system using YOLOv8, DeepSORT, and OpenCV with persistent ID tracking and live video processing.

YOLOv8 Object Detection & DeepSORT Tracking App built with Streamlit — supports image detection, video analysis, and real-time webcam tracking

Object Tracking System using YOLOv8 + DeepSORT. Detects objects and tracks them across frames. Applications include counting people, monitoring traffic, and tracking players in sports. Built with YOLOv8, OpenCV, and DeepSORT for accurate real-time tracking.

A real-time object detection and multi-object tracking system using YOLOv8 and DeepSORT, with a modern React + FastAPI web interface.

Real-time multi-object detection, tracking, and counting system using YOLOv8 and DeepSORT, with performance benchmarking and visual analytics dashboard.

Developed a real-time AI surveillance system using YOLOv8 and DeepSORT for object detection and multi-object tracking with unique ID assignment via webcam

This project combines YOLOv8 segmentation with the DeepSORT tracking algorithm to achieve real-time multi-object tracking with instance segmentation. It detects and segments objects frame by frame, while DeepSORT assigns unique IDs to maintain consistent object tracking across video streams.

Real-time multi-object tracking pipeline with YOLOv8 detection and ByteTrack/DeepSORT association. Evaluated on MOT17 benchmark with MOTA/IDF1 metrics. Includes Streamlit demo.

WasteVision AI: YOLOv8-powered object detection with DeepSORT tracking for real-time waste classification and monitoring. Designed to enable smarter, sustainable, and automated waste management.

This project implements a real-time object detection and tracking system using a pre-trained YOLOv8 model and DeepSORT tracker. It processes video inputs, detects objects with bounding boxes, tracks unique objects across frames, and generates analytics reports.

"Real-time object detection and tracking using YOLOv8 and DeepSORT. Detects and tracks specified objects (e.g., persons, motorcycles) in video streams, assigning unique IDs for continuous tracking. Outputs video with bounding boxes and track IDs. Ideal for surveillance, traffic monitoring, and object counting."

Real-time Object Detection and Tracking using YOLOv8 and DeepSORT. This project detects multiple objects through a webcam, assigns each a unique ID, and tracks their movement with labels and directions using Python, OpenCV, and AI models.

A **production-grade**, real-time computer vision pipeline that combines **YOLOv8** detection with **DeepSORT** multi-object tracking. Features persistent tracking IDs, per-class colour coding, FPS overlay, confidence filtering, and a clean CLI.

A real-time object tracking pipeline utilizing YOLOv8 for detection and DeepSORT for association. Features a custom Siamese CNN trained with Triplet Loss for robust re-identification of objects with erratic, non-linear motion patterns.

Real-time car detection and tracking in videos using YOLOv8 for object detection and DeepSORT for multi-object tracking. This project processes video streams, detects cars, tracks their movements with unique IDs, and saves the output as an annotated video with bounding boxes and tracking information. Customizable for other object classes."

Developed a CPU-optimized multi-object tracking system using YOLOv8 and DeepSORT to track vehicles and pedestrians across video frames with unique IDs, deployed through an interactive FastAPI-powered dashboard for real-time traffic analytics.

YOLO-DeepSORT-Vehicle-Tracking integrates YOLOv8 for real-time vehicle detection with DeepSORT for multi-object tracking. Designed for urban traffic monitoring, it assigns unique IDs to vehicles across frames, enabling precise counting and movement analysis. Ideal for applications in smart cities, surveillance, and traffic analytics.

A real-time aircraft tracking system combining YOLOv8 object detection with DeepSORT algorithm to maintain persistent IDs across 46 aircraft classes. Features GPU-accelerated processing, CSV analytics export, and annotated video output generation.

Real-time multi-object tracking using YOLOv8 + DeepSORT. Supports vehicle & pedestrian detection with unique track IDs, motion trails, per-class counts, and FPS stats. Works on webcam or video files with optional MP4 output. Built with Python, OpenCV & Ultralytics.

AI-based real-time object detection and tracking system using YOLOv8 and DeepSORT. It detects and tracks multiple objects with unique IDs, performs people counting, entry/exit detection, crowd alerts, saves video and data to CSV, and includes heatmap visualization with a Streamlit dashboard for analysis.