Achieving robustness in image segmentation models is challenging due to the fine-grained nature of pixel-level classification. These models, which are crucial for many real-time perception applications, particularly struggle when faced with natural corruptions in the wild for autonomous systems. While sensitivity analysis can help us understand how input variables influence model outputs, its application to natural and uncontrollable corruptions in training data is computationally expensive. In this work, we present an adaptive, sensitivity-guided augmentation method to enhance robustness against natural corruptions. Our sensitivity analysis on average runs 10 times faster and requires about 200 times less storage than previous sensitivity analysis, enabling practical, on-the-fly estimation during training for a model-free augmentation policy. With minimal fine-tuning, our sensitivity-guided augmentation method achieves improved robustness on both real-world and synthetic datasets compared to state-of-the-art data augmentation techniques in image segmentation.

We present an immersive gaming demonstration using the minimum amount of wearable sensors. The game demonstrated is two-player tennis. We combine a virtual environment with real 3D representations of physical objects like the players and the tennis racquet (if available). The main objective of the game is to provide as real an experience of tennis as possible, while also being as less intrusive as possible. The game is played across a network, and this opens the possibility of two remote players playing a game together on a single virtual tennis pitch. The Microsoft Kinect sensors are used to obtain a 3D point cloud and a skeletal map representation of the player. This 3D point cloud is mapped on to the virtual tennis pitch. We also use a wireless wearable Attitude and Heading Reference System (AHRS) mote, which is strapped onto the wrist of the players. This mote gives us precise information about the movement (swing, rotation etc.) of the playing arm. This information along with the skeletal map is used to implement the physics of the game. Using this game we demonstrate our solutions for simultaneous data acquisition, 3D point-cloud mapping in a virtual space, use of the Kinect and AHRS sensors to calibrate real and virtual objects and for interaction of virtual objects with a 3D point cloud.