Vehicle Detection Overview This project implements a vehicle detection and counting system using OpenCV and Python. It processes a video file, detects moving vehicles, and counts them when they pass a predefined
This paper presents a vehicle detection model predicated on convolution neural network using bounding box annotations for marking the region of interest. The model is tuned to give best performance by evaluating the different parameter configurations. The implementation is done using Python and OpenCV ...
Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG). - JunshengFu/vehicle-detection
OpenCV Scikit-Learn Matplotlib (Optional) How to Run the Vehicle Detector? python main.py Implementation Vehicle Detection Pipeline The following figure shows the vehicle detection pipeline we used for the project. As the picture depicted, pipeline starts with the sliding window stage. Next, the ext...
The primary software environment included Python 3.10, Pytorch 2.2.1 + CUDA11.8, Open3D 0.18.0, and OpenCV 4.9.0. During training, the batch size was set to 32, the learning rate to 0.001, and the number of epochs to 10. The enhanced vehicle JPG dataset was loaded and pre-...
完成。 /home/aistudio/work/PaddleDetection 安装python依赖模块: In [8] !pip install -r requirements.txt Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r ...
sh ~/vehicle_detection_haarcascades/run_vehicle_detection_video2.sh Docker image Docker image is available at: * Ubuntu 16.04 + VNC + OpenCV 2.4.13 + Python 2.7 + Vehicle Detection, Tracking and Countinghttps://hub.docker.com/r/andrewssobral/vehicle_detection_tracking_counting/ Release Notes:...
The software experiment platform is CUDA 11.4, cuDNN 8.2.2, the deep learning framework is Darknet and PyTorch 1.9.1, the integrated development environment is PyCharm, the programming language is Python, and the visualization is based on OpenCV. Like YOLO v5, YOLOX networks also use depth ...
~/vehicle_detection_haarcascades/run_vehicle_detection_video1.sh ~/vehicle_detection_haarcascades/run_vehicle_detection_video2.sh Docker imageDocker image is available at: Ubuntu 16.04 + VNC + OpenCV 2.4.13 + Python 2.7 + Vehicle Detection, Tracking and Counting https://hub.docker.com/r/...
This example demonstrates how to use VehicleDetectionTracker to process a real-time video stream using OpenCV. Simply provide the URL of the video stream to get started:from VehicleDetectionTracker.VehicleDetectionTracker import VehicleDetectionTracker video_path = "[[YOUR_STREAMING_SOURCE]]" vehicle_...