Video analytics leverages deep learning to monitor real-time video streams, detect objects and movement patterns, and extract insights to enable smart decisions, security for homes, cities, and transportation.

Use the get and set methods to query and update video capture properties, such as frame width and height, with examples like 640 and 480, and 320 and 240.

The cap.read() method reads each video frame, returning a success flag and the frame image. If the read is successful, proceed with operations; otherwise print an error and break.

Explore grab and retrieve methods in video processing for multi-camera environments without synchronization. Call grab on each camera, then retrieve to decode frames and align timing, reducing motion jpeg decompression.

Trace the timeline of popular video codecs from MJPEG to H.265. Explore how each codec enabled DVD, Blu-ray, and UHD streaming with improved compression.

Identify and locate humans in images and videos by extracting features that quantify the human body and bounding boxes, then apply trained machine learning models to detect and track them.

Explore the histogram of oriented gradients (hog) model for human detection in videos, using gradient-based global features, a sliding detection window, 8x8 pixel blocks, and an svm classifier.

Study faster r-cnn and r-fcn for video object detection, comparing region proposal networks with region proposals via region of interest scoring, enabling detection and achieving mAP on the Pascal dataset.

Define a social distancing solution using object detection models and provide a detailed code walkthrough, tool setup, and downloadable resources for haar cascade, hog, yolov3 tiny, and faster r-cnn.

Open the Ubuntu terminal to download Python 3.6 using the on-screen command, then install OpenCV with pip3 and Jupyter Notebook using the exact on-screen syntax.

Move to the Windows environment by installing Python 3.6 in the C drive, then install OpenCV with pip and the OpenCV contrib package, and finally install Jupyter Notebook.

Download the Haar cascade xml file and social distancing python code from the resource section, and run a lightweight person detection model on the input video with minimal hardware.

Walks through looping over layer outputs and detections, extracting class id and confidence, thresholding at 0.5, scaling YOLO boxes to coordinates, and applying non maximum suppression to prepare distance computations.

Explore a YOLOv3 tiny social distancing solution with Python code, using YOLOv3 tiny weights, cfg, and COCO names from the resources, and view the output video.

Download and run the faster r-cnn social distancing project: unzip faster-rcnn.zip, execute faster_rcnn_Social_Distancing.py with frozen_inference_graph.pb, requirements.txt, and input video, comparing Haar Cascade, Hog, YoloV3 Tiny, Faster R-CNN.

Explore image classification in computer vision, which assigns a label to an image based on visual content and trains models to recognize target classes like cats, dogs, or handwritten digits.

Click the black arrow on each block in google colab to run code, and start the walkthrough by importing tensorFlow and keras for model creation.

Set up variables for pre-processing and training with Keras ImageDataGenerator to rescale images and load and resize data via flow_from_directory for training and validation.

Upload a test image to Colab, load the trained model, and classify the image to yield a positive or negative value, where positive means mask and negative means no mask.

Open the Ubuntu terminal, install Python 3 and pip, install OpenCV, verify with Python 3, and install TensorFlow 2.2 or newer to set up tools for video object detection.

Pass detected face images to a classification model for prediction, yielding positive values for masked faces and negative for unmasked, and store results with the input images and face coordinates.

Learn to implement the SORT framework for real-time multi-object tracking by detection, using Kalman filter and the Hungarian algorithm, with unique ids and occlusion handling for footfall and parking analyses.

Download and unzip the license plate detection yolov3.zip from resources, open in PyCharm to access main.py and model.py, then run main.py with the test_dataset and YOLO_utils (config, weights, class names).