HACKFORCAUSE 2.0

POST-COVID DIGITIZATION

Behaviour detection system using openCV

​

Team Name : 0x5A

​

Team ID : T2120

Team

• Raghavendra Achar C
• Tirtharaj G
• Pranay Kumar Andra
• Ishaq Shaik

Design of a behaviour detection system based on using openCV

Analysis on the background characteristics of combined visual appearance, behavioural detection algorithm based on simulation and optimization of using OpenCV open source Visual Database design and implementation of a prototype system, the system can provide support and results for the teachers, so as to minimize the ill being of the students from happening.

TatT

Tech Stack

FRONTEND

React JS

Material UI

​

​

​

​

DEPENDENCIES

SciPy

DATABASE

MongoDB

NumPy

Imutils

cv2

Tech Stack

Haar Cascade Algorithm.

​

Student achievement depends on teachers, education programs, learning environment, study hours, academic infrastructure, institutional climate and financial issues. Another extremely important factor is the learner’s behavior including study skills, study attitude, and motivation, to have strong interaction with students learning results.

Students perceptions of the teaching and learning environments influence their study behavior. This means if teachers can grasp the bad attitudes of students, they can make more reasonable adjustments to change the learning environment for the students.

To conclude whether good or bad behavior for a particular student is not an easy problem to solve, it must be identified by the teacher who has worked directly in the real environment. The teacher can track student behavior by observing and questioning them in the classroom. This process is not difficult in a classroom that has few students, but it is a big challenge for a classroom with a large number of students. It is valuable to develop an effective tool that can help teachers and other roles to collect data of student behavior accurately without spending too much human effort, which could assist them in developing strategies to support the learners. In this way, the students performances could be increased.

​

Main Goals

• Eliminating the problem caused by the lightning by stabilising the face landmark points or coordinates.
• Extracting the face and going ahead for further processing.
• Making sure the image processing seems remarkable and the output remains extravagant.
• Real time setting of facial behaviour analysis.
• Maintaining the facial gestures made by the peer for further research.
• Implementing Haar Cascade Algorithm.
• Making our own Cascade classifiers.

​

​

References

• Wikipedia
• Google
• Paul Viola and Michael J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, 2004. (Research Paper)
• Modeling antecedents of university students study behavior
• Cascade classifiers

​

THANK YOU

​

0x5A | T2120