Day #280 - Human detection and Tracking

Project #1 - Human Detection and Tracking 

Overview

• Detecting a human and its face in a given video and storing Local Binary Pattern Histogram
• Recognize them in any other videos
• Local Binary Pattern Histogram - type of visual descriptor, clockwise direction check neighbour values

Execution Steps
Clone the project
Step 1 - python create_face_model.py -i data
Step 2 - python main.py -v video

Project #2 - Person-Detection-and-Tracking  (Pending Execution)

Overview

• The person detection in Real-time is done with the help of Single Shot MultiBox Detector
• Single Shot MultiBox Detector
• Tracking - Kalman Filter is fed with the velocity, position and direction of the person which helps it to predict the future location 

Single Shot MultiBox Detector

• The core of SSD is predicting category scores and box offsets for a fixed set of default bounding boxes using small convolutional filters applied to feature maps
• The key difference between training SSD and training a typical detector that uses region proposals, is that ground truth information needs to be assigned to specific outputs in the fixed set of detector outputs

Execution Steps
Clone the project https://github.com/ambakick/Person-Detection-and-Tracking

Execute - camera.py in Spyder

Project #3 - Tracker Types Demo Project (Pending Execution)

Overview

• Track Multiple faces
• Download and Experiment

Run Below Demos
demo - track multiple faces.py
Multiple_Trackers.py
face_eye.py
distance_to_camera.py

Datasets - Link

Object Motion Detection and Tracking for Video Surveillance
Measuring size and distance with OpenCV
Calculate X, Y, Z Real World Coordinates from Image Coordinates using OpenCV

Happy Learning!!!