To secure our assets and protect our privacy, there is a strong demand for user-friendly biometric security systems. There are various types of biometric systems that use signatures, fingerprints, voice, hand geometry, ear geometry, face detection and so on. Among these, face recognition appears to be quite exciting and is catching attention.
Recognizing vehicle number plates is a difficult but much needed system. This is very useful for automating toll booths, automated signal breakers identification and finding out traffic rule breakers. The aim of the project is to recognize the number plate of vehicle which passes through system and capture image by camera automatically by using raspberry pi. When number plate is recognized at that time gate will be opened and when number plate is not recognized gate will remain closed.
Here we propose a Raspberry Pi based vehicle number plate recognition system that automatically recognizes vehicle number plates using image processing. The system uses a camera along with LCD display circuit interfaced to a Raspberry pi. The system constantly processes incoming camera footage to detect any trace of number plates. On sensing a number plate in front of the camera, it processes the camera input, extracts the number plate part from the image. Processes the extracted image using OCR and extracts the number plate number from it. The system then displays the extracted number on an LCD display. A gate operation is done by driving a dc motor, Thus we put forward a fully functional vehicle number plate recognition system using Raspberry Pi.
The main objective of our project is to develop a Machine Learning based system that uses gestures to perform functions. There are various gesture based applications wherein it need to learn the predefined gestures and also there is limited functionality.
In this project we will develop a system that can be trained to recognize the gestures by performing the SVM library function in Python script. Our main aim to demonstrate this by training our system to recognize letters by the gestures we make in air.
The system design involved an Arduino Board interfaced with an accelerometer/gyroscope MPU6050 series sensor. This sensor can be attached to the user’s hand. The accelerometer sensor will provide motion axis data which input to the microcontroller about the hands gestured coordinates. A Bluetooth module is also interfaced to the microcontroller which transfer these data to computer run Python script. The Python algorithm will receive these data through wireless blue tooth transmission and maintain a database to recognize each gesture differently. Once we train the system with the same gesture multiple times it will gather enough data to have an estimate of what the gesture should look like.
Instead of using a keyboard, mouse or joystick, we can use our hand gestures to control certain functions of a computer like play/pause a video, move left/right in a photo slide show, scroll up/down in a web page and many more. This project is aimed to develop an Arduino microcontroller based hand gesture control of computer where one can control few functions of web browser like switching between tabs, scrolling up and down in web pages, shift between tasks (applications), play or pause a video and increase or decrease the volume (in VLC Player) with the help of hand gestures.
This project Arduino based Hand Gesture Control of Computer is implemented using Python and is used two Ultrasonic Sensors interfaced with Arduino, while place our hand in front of the Ultrasonic Sensor it calculate the distance between the hand and the sensor. Using this information, relevant actions in the computer can be performed. The position of the Ultrasonic Sensors is very important. Place the two Ultrasonic Sensors on the top of a laptop screen at either end. The distance information from Arduino is collected by a Python Program and a special library called PyAutoGUI will convert the data into keyboard click actions.
In this project, the process of obtaining intravenous (IV) access, Venipuncture, is an everyday invasive procedure in medical settings and there are more than one billion venipuncture related procedures like blood draws, peripheral catheter insertions, intravenous therapies, etc. Excessive venipunctures are both time and resource consuming events causing anxiety, pain and distress in patients, or can lead to severe harmful injuries . The major problem faced by the doctors today is difficulty in accessing veins for intra-venous drug delivery & other medical situations . There is a need to develop vein detection devices which can clearly show veins. This project deals with the design development of non-invasive subcutaneous vein detection system and is implemented based on near infrared imaging and interfaced to a laptop to make it portable. A customized CCD camera and infrared light source are used for capturing the vein images and significantly increase the venous pattern using the adaptive histogram equation method (CLHA) with limited contrast and applying the different image filtering methods using MATLAB Image processing functions. This simple approach can successfully lead to bleeding, kicks, etc.