Computer Vision - Object Tracking with OpenCV and Python

Description

In this project I have covered 6 types of Optical Image Tracking based on the image namely : 1: Optical Flow 2: Dense Optical Flow 3: MeanShift 4: CamShift 5: Single Object Tracking 6: Multi Object Tracking

Inplimentations

1. BOOSTING Tracker: Based on the same algorithm used to power the machine learning behind Haar cascades (AdaBoost), but like Haar cascades, is over a decade old. This tracker is slow and doesn’t work very well. Interesting only for legacy reasons and comparing other algorithms. (minimum OpenCV 3.0.0)
2. MIL Tracker: Better accuracy than BOOSTING tracker but does a poor job of reporting failure. (minimum OpenCV 3.0.0)
3. KCF Tracker: Kernelized Correlation Filters. Faster than BOOSTING and MIL. Similar to MIL and KCF, does not handle full occlusion well. (minimum OpenCV 3.1.0)
4. CSRT Tracker: Discriminative Correlation Filter (with Channel and Spatial Reliability). Tends to be more accurate than KCF but slightly slower. (minimum OpenCV 3.4.2)
5. MedianFlow Tracker: Does a nice job reporting failures; however, if there is too large of a jump in motion, such as fast moving objects, or objects that change quickly in their appearance, the model will fail. (minimum OpenCV 3.0.0)
6. TLD Tracker: I’m not sure if there is a problem with the OpenCV implementation of the TLD tracker or the actual algorithm itself, but the TLD tracker was incredibly prone to false-positives. I do not recommend using this OpenCV object tracker. (minimum OpenCV 3.0.0)
7. MOSSE Tracker: Very, very fast. Not as accurate as CSRT or KCF but a good choice if you need pure speed. (minimum OpenCV 3.4.1)
8. GOTURN Tracker: The only deep learning-based object detector included in OpenCV. It requires additional model files to run.(attached) (minimum OpenCV 3.2.0)

Prerequisites

You need to have installed following softwares and libraries in your machine before running this project.

1. Python 3
2. Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, PIL.
3. OpenCV: Image processing library.

Data

Download Goturn caffemodel from https://github.com/Mogball/goturn-files

1. 4 videos for different objct tracking scenarios.
2. 'haarcascade_frontalface_default.xml' for face detection.
3. GoTurn Model.

Installing

1. Python 3: https://www.python.org/downloads/
2. Anaconda: https://www.anaconda.com/download/
3. OpenCV: pip3 install opencv-python
4. Keras: pip3 install keras
5. flask: pip3 install flask
6. goturn(.caffemodel and .prototxt): Attached in the folder : Data/.

Built With

• ipython-notebook - iPython Text Editor
• OpenCV - It is used for processing images
• Flask - Flask is a micro web framework written in Python. It is a lightweight WSGI web application framework.
• GoTurn(Caffe model) - Model for effective object tracking.

Authors

Made with ❤️ by Rohan Dubey - Complete work