Multiple-Object-Tracking-using-Centroid-Tracking-Algorithm-and-openCV4.0

Multiple object tracking mechanism achieved using centroid tracking algorithm and openCV4.0.

Libraries Installation:

1. OpenCV4.0: Refer this: https://www.pyimagesearch.com/opencv-tutorials-resources-guides/

2. numpy/argparse/imutils/scipy: Do pip install numpy/argparse/imutils/scipy

An ideal object tracking algorithm will:

Only require the object detection phase once (i.e., when the object is initially detected)
Will be extremely fast — much faster than running the actual object detector itself
Be able to handle when the tracked object “disappears” or moves outside the boundaries of the video frame
Be robust to occlusion
Be able to pick up objects it has “lost” in between frames

The centroid tracking algorithm

The centroid tracking algorithm is a multi-step process.

STEP 1: Compute Euclidean distance between new bounding boxes and existing objects

STEP 2: Update (x, y)-coordinates of existing objects

STEP 3: Register new objects

STEP 4: Deregister old objects

Run command:

python object_tracker.py --prototxt deploy.prototxt --model res10_300x300_ssd_iter_140000.caffemodel