Personal Portfolio of Sajanraj Thandassery

Bridging AI Research and Industrial Impact for the Future of Mobility.

Data is reshaping mobility. At the intersection of machine learning and logistics, we’re building transport systems that don’t just react—but anticipate, adapt, and evolve. This is data science in motion, driving real-world impact at scale.

HPC Specialist Postdoctoral Researcher (KTP) High Performance Computing Data Science & AI

The transportation system utilizes various methods to analyze passenger flow effectively. Although passenger information is accessible through the automated fare collection system, accurate live passenger counts at specific points require an onboard sensor system. The proposal emphasizes the necessity of a camera-based system, utilizing computer vision technology to count passenger. The paper also examines various computer vision methods, depth sensors, and tracking algorithms for implementing passenger counters in the transport sector. The proposed work introduced a wide-angle TOF sensor-based passenger tracking algorithm and obtained an accuracy of 98%. The method also explores a multi-tracking scenario after eliminating multiple sensors.

The passenger information system in railway sector is essential for helping passengers with schedules, connectivity, and overall comfort. It delivers on-board passenger information for trams, trains, and buses as an innovative solution with real-time information. Our proposed method utilizes an edge computing based computer vision algorithm to accurately count passenger data on an embedded computing board, including the number of passengers entering and exiting, as well as the current load in each carriage. This information is then presented in a way that helps passengers choose the carriage or route that best maintains their comfort. The system’s design and development, from the sensor device to the API specification and its visual representation, are developed as part of a comprehensive product development process. The proposed method tested with an accuracy of 98% using data collected from a wide angle Time-of-Flight (TOF) camera-based counting system that utilized passenger detection and tracking.

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning methods in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8’s real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras.