chs74515's repositories
PeopleCounter
In present days, people detection, tracking and counting is an important aspect in the video investigation and subjection demand in Computer Vision Systems. Providing (real time) traffic information will help improve and reduce pedestrian and vehicle traffic, especially when the data collected is learned and analyzed over a period of time, which makes its highly essential to identify people, vehicles and objects in general and also accurately counting the number of people and/or vehicles entering and leaving a particular location in real time. To perform people counting, a robust and efficient system is needed. This research is aimed at making a pedestrian traffic reporting system for certain areas and buildings around the campus to potentially help ease traffic circulation. Providing this information will be done through a developed application, which includes image processing with Open Computer Vision (OpenCV). This will show the amount of traffic in certain buildings or area over a period of time. OpenCV is a cross-platform library which can be used to develop real-time Computer Vision applications [Opencv, 2015b]. It is mainly focused on image processing, video capture and analysis including features like people and object detection. The operations performed were based on the performance and accuracy of the tracking algorithms when implemented in embedded devices such as the Raspberry Pi and the Tinker Board. The Pi Camera was used for real time vision and hosted on the embedded device. The proposed method used was conjoined with an open-source visual tracking implementation from the contribution branch of the OpenCV library and a unique technique for people detection along with different Filtering Algorithms for tracking this. The programming language of choice to implement these features (Tracking and Detection) is python and its libraries. The present work describes a standalone people counting application designed using Python OpenCV and tested on embedded devices ranging from the Raspberry Pi3 to a Tinker Board and a compatible Camera. All these were used in prototyping the design of this application. The results reported and showed that the Person-Counter system developed counted the number of people entering the designated area (down), and the number of people leaving (up).
Smart_Home_Latest_Version
This project will seek to explore the interaction between users and systems of smart devices and how the interface for this interaction can be enhanced. In order to gain insight into the design and implementation of a smart home hub, we designed and created a smart home system with a Raspberry Pi acting as the central hub. Our project aims to use this system as a platform to consider alternatives and supplements to the traditional smart home system interface in order to improve its usefulness. These alternatives include technology such as voice recognition and motion control
SpringHealthyEhrAnalysis
A Spring Boot Application with a Thymeleaf Frontend. Java + Spring Boot framework. MVC and Data transfer Object Design Pattern DTOD), Using an open-source REST client - Spring REST template.
railsfriendsApp
Friends App using Ruby on Rails. Fun with MVC User management, CRUD Scaffold db MIGRATION, Devise GEM, Association (Active Records), Bootstrap Style Modification
Stocks-Datawarehousing-Project-stockify-
Stockify. A stock performance analyzer application to visualize stocks data of a warehouse. The goal of this project is to develop a data warehouse for analyzing the trend of the financial market and maximizing users’ investments. The type of stocks I chose to first analyze was the US stock prices of the End of Day data. Here is what the data warehousing schema looks like (from initial design this can be changed) for financial market analysis. Finally, I have implemented a web application of analytical models for forecasting information to help the decision-making of the users. Read the Project Report file for more info