Yolo algorithm for detection + centroid tracker to track vehicles

Last update: Dec 21, 2022

Related tags

Deep Learning Vehicle-tracking

Overview

Vehicle Tracking using Centroid tracker

Algorithm used : Yolo algorithm for detection + centroid tracker to track vehicles

Backend : opencv and python

Library required:

opencv = '4.5.4-dev'
scipy = '1.4.1'

IMPORTANT:

I hadn't uploaded model weights and configuration files (which were used for object detection) here because those were already available in yolo_detection repo
download yolo tiny weights , config file and coco.names file from here : [https://github.com/hasit73/yolo_detection]
For detection i was using same code which was available in yolo_detection repo.

Quick Overview about structure

1) main.py

Loading model and user configurations
perform interfacing tasks

2) yolo.py

use opencv modules to detect objects from user given media(photo/video)
detection take place inside this file

3) config.json

user configuration are mentioned inside this file
for examples : input shapes and model parameters(weights file path , config file path etc) are added in config.json

4) centroid_tracker.py

implementation of centroid tracker

How to use

clone this directory
use following command to run detection and tracking on your custom video

python main.py -c config.json -v

Example:

python main.py -c config.json -v car1.mp4

Note : Before executing this command make sure that you have downloaded model weights and config file for yolo object detection.

Results

output:1

demo.mp4

output:2

demo1.mp4

Limitations:

There are two primary drawbacks of this object tracking algorithm.

The first is that it requires that object detection step to be run on every frame of the input video.
The second drawback is related to the underlying assumptions of the centroid tracking algorithm itself — centroids must lie close together between subsequent frames.

If it's helful for you then please give star :)

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Comments

Question about index for input centroids

If I understand this right, this line is for giving indices of input centroids (cols' indices of distance matrix) whose distances are min w.r.t previous centroids https://github.com/hasit73/Vehicle-tracking/blob/99ad5f3ddb91c114912d7b3ed887036e94c77db7/centroid_tracker.py#L56

my question is that since we already have the rows, can we simple use sth like cols = list(range(distance_matrix.shape[1])) to get the indices for col? Even though this cannot give desire output when there are identical distances, but so are argmin function.

opened by RicoSuaveGuapo 0