Research Article
Enhancing Autonomous Vehicle Navigation: Traffic Police Hand Gesture Recognition for Self-Driving Cars in India using MoveNet Thunder
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357997, author={C. Vasuki and Sanjay R and Janani Priya S and Sathiyanathan V}, title={Enhancing Autonomous Vehicle Navigation: Traffic Police Hand Gesture Recognition for Self-Driving Cars in India using MoveNet Thunder}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={traffic gesture recognition autonomous vehicles movenet thunder tensorflow real-time detection carla simulator sensor fusion indian traffic}, doi={10.4108/eai.28-4-2025.2357997} }- C. Vasuki
Sanjay R
Janani Priya S
Sathiyanathan V
Year: 2025
Enhancing Autonomous Vehicle Navigation: Traffic Police Hand Gesture Recognition for Self-Driving Cars in India using MoveNet Thunder
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2357997
Abstract
This Traffic Police Hand Gesture Recognition System for autonomous vehicles builds on the TensorFlow’s MoveNet Thunder model. The hand gestures recognized in the system include 'Stop', 'Turn Left' and 'Move Forward', all common gestures used by traffic police in India. The custom dataset consisted of 8,000 images with different gestures under diverse conditions. We employ dense and dropout layers in our neural network architecture to achieve a high accuracy and avoid overfitting. Haar cascades are used for real time face detection, so gestures are recorded only when the officer is facing the camera. The accuracy of the model is 89%, while in most cases of the classes it is well across, but there were a few minor misclassifications between similar gestures. The system was validated using the Carla simulator with and without weather and lighting conditions. A prototype of this solution is shown to be promising for safe and efficient integration into autonomous vehicle systems for navigation in traffic-managed environments.
