Abstract-This paper presents the autonomous tracking and following of a marine vessel by an Unmanned Surface Vehicle in the presence of dynamic obstacles while following the International Regulations for Preventing Collisions at Sea (COLREGS) rules. The motion prediction for the target vessel is based on Monte-Carlo sampling of dynamically feasible and collisionfree paths with fuzzy weights, leading to a predicted path resembling anthropomorphic driving behavior. This prediction is continuously optimized for a particular target by learning the necessary parameters for a 3-degree-of-freedom model of the vessel and its maneuvering behavior from its path history without any prior knowledge. The path planning for the USV with COLREGS is achieved on a grid-based map in a single stage by incorporating A* path planning with Artificial Terrain Costs for dynamically changing obstacles. Various scenarios for interaction, including multiple civilian and adversarial vessels, are handled by the planner with ease. The effectiveness of the algorithms has been demonstrated both in representative simulations and on-water experiments.

Images of static scenes submerged beneath a wavy water surface exhibit severe non-rigid distortions. The physics of water flow suggests that water surfaces possess spatiotemporal smoothness and temporal periodicity. Hence they possess a sparse representation in the 3D discrete Fourier (DFT) basis. Motivated by this, we pose the task of restoration of such video sequences as a compressed sensing (CS) problem. We begin by tracking a few salient feature points across the frames of a video sequence of the submerged scene. Using these point trajectories, we show that the motion fields at all other (non-tracked) points can be effectively estimated using a typical CS solver. This by itself is a novel contribution in the field of non-rigid motion estimation. We show that this method outperforms state of the art algorithms for underwater image restoration. We further consider a simple optical flow algorithm based on local polynomial expansion of the image frames (PEOF). Surprisingly, we demonstrate that PEOF is more efficient and often outperforms all the state of the art methods in terms of numerical measures. Finally, we demonstrate that a two-stage approach consisting of the CS step followed by PEOF much more accurately preserves the image structure and improves the (visual as well as numerical) video quality as compared to just the PEOF stage.

No abstract

No abstract

Deaf children born to hearing parents lack continuous access to language, leading to weaker working memory compared to hearing children and deaf children born to Deaf parents. CopyCat is a game where children communicate with the computer via American Sign Language (ASL), and it has been shown to improve language skills and working memory. Previously, CopyCat depended on unscalable hardware such as custom gloves for sign verifcation, but modern 4K cameras and pose estimators present new opportunities. Before re-creating the CopyCat game for deaf children using of-the-shelf hardware, we evaluate whether current ASL recognition is sufcient.Using Hidden Markov Models (HMMs), user independent word accuracies were 90.6%, 90.5%, and 90.4% for AlphaPose, Kinect, and MediaPipe, respectively. Transformers, a state-of-the-art model in natural language processing, performed 17.0% worse on average.Given these results, we believe our current HMM-based recognizer can be successfully adapted to verify children's signing while playing CopyCat. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; Accessibility technologies; • Applied computing → Computer-managed instruction; • Computing methodologies → Machine learning; Feature selection.