Dipankar Nandi

Generation of 3D Scene using Unity3D Software for Autonomous Driving Development of Point Cloud Processing Pipelines for 3D Object Recognition & Tracking Reconstruction of Indoor Environment using Neural Radiance Field & Unity3D Object and Keypoint Annotation using CVAT, LabelMe Tool

Project1 Annotated a TopView Dataset using CVAT and further corrected it by 30% Implemented a Deep Learning pipeline with CenterNet for Human Pose and Object Detection Refined the accuracy from the initial 33% to around 51% and maximized to 65% Project2 Assisted in Soil moisture sensor data collection and processing using SQL and Pandas Sensor Data reporting with Microsoft Power BI Developed Random Forest, SVM, and CNN predictive models with an accuracy of around 67%, 56%, and 73% respectively

Assisted in Camera Calibration and Data correction on omnidirectional images, improving the dataset by 40% Presented a comparative study between SVM, K-means, and CNN algorithms Achieved a precision value of 60% for Human Activity Recognition by constructing a CNN-based model

Executed Data collection and cleaning for ANN and CNN architectures for Object Recognition and Localization Deployed a YOLOv3 architecture to show an improved accuracy of 20% over Fast-RCNN implementation

2 Projects: Parking Space Detection and Customer Sentiment Analysis Engineered a Parking Inspection system using Image processing(OpenCV) and Machine Learning algorithms Integrated the Parking system within an IoT environment(LoRaWAN) to ease the tracking process by 45% Improved market analysis accuracy by 25% through SQL, Pandas and Seaborn Visualized and prepared a dashboard using Power BI Developed 2 Regression-based models with an accuracy of 73% to improve customer-market sentiment

Thesis ( Ongoing ): Top-View Synthesis and Ground Truth Estimation using Neural Field Radiance on Humans CourseWork: Computer Vision and Neural Networks Programming and Data Analysis Image processing and Pattern Recognition