Emil Hovad

Emil Hovad

We introduce a Stability-Aware Frozen Euler autoencoder for Physics-Informed Tracking in Continuum Mechanics (SAFE-PIT-CM) that recovers material parameters and temporal field evolution from videos of physical processes. The architecture is an autoencoder whose latent-space transition is governed by a frozen PDE operator: a convolutional encoder maps each frame to a latent field; the SAFE operator propagates it forward via sub-stepped finite differences; and a decoder reconstructs the video. Because the physics is embedded as a frozen, differentiable layer, backpropagation yields gradients that directly supervise an attention-based estimator for the transport coefficient alpha, requiring no ground-truth labels. The SAFE operator is the central contribution. Temporal snapshots are saved at intervals far larger than the simulation time step; a forward Euler step at the frame interval violates the von Neumann stability condition, causing alpha to collapse to an unphysical value. The SAFE operator resolves this by sub-stepping the frozen finite-difference stencil to match the original temporal resolution, restoring stability and enabling accurate parameter recovery. We demonstrate SAFE-PIT-CM on the heat equation (diffusion, alpha < 0) and the reverse heat equation (mobility, alpha > 0). SAFE-PIT-CM also supports zero-shot inference: learning alpha from a single simulation with no training data, using only the SAFE loss as supervision. The zero-shot mode achieves accuracy comparable to a pre-trained model. The architecture generalises to any PDE admitting a convolutional finite-difference discretisation. Because latent dynamics are governed by a known PDE, SAFE-PIT-CM is inherently explainable: every prediction is traceable to a physical transport coefficient and step-by-step PDE propagation.

Emil Hovad, Allan Peter Engsig-Karup

We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder localizes a particle as a heatmap peak (landmark) and a differentiable physics module embedded in the autoencoder constrains several landmarks over time (a trajectory) to satisfy known dynamics. The novel Physics-Informed Landmark Loss (PILL) compares this predicted trajectory back against the landmarks, enforcing physical consistency without labels. Its supervised variant (PILLS) instead compares the prediction against ground-truth position, velocity, and bounce from simulation, enabling end-to-end backpropagation. To support supervised and unsupervised learning, we use an autoencoder with a split bottleneck that separates A) tracking-related structure via landmark heatmaps from B) background noise and subsequent image reconstruction. We evaluate a replicated 26 factorial design (n = 4 replicates, 64 configurations), showing that PILLS consistently achieves sub-pixel tracking accuracy for the bilinear and physics-refined decoder outputs under both clean and noisy conditions.

Moamal Fadhil Abdul, Jonas Bruun Hubrechts, Thomas Martini Jørgensen et al.

Automatically detecting and classifying strokes in table tennis video can streamline training workflows, enrich broadcast overlays, and enable fine-grained performance analytics. For this to be possible, annotated video data of table tennis is needed. We extend the public OpenTTGames dataset with highly detailed, frame-accurate shot type annotations (forehand, backhand with subtypes), player posture labels (body lean and leg stance), and rally outcome tags at point end. OpenTTGames is a set of recordings from the side of the table with official labels for bounces, when the ball is above the net, or hitting the net. The dataset already contains ball coordinates near events, which are either "bounce", "net", or "empty_event" in the original OpenTTGames dataset, and semantic masks (humans, table, scoreboard). Our extension adds the types of stroke to the events and a per-player taxonomy so models can move beyond event spotting toward tactical understanding (e.g., whether a stroke is likely to win the point or set up an advantage). We provide a compact coding scheme and code-assisted labeling procedure to support reproducible annotations and baselines for fine-grained stroke understanding in racket sports. This fills a practical gap in the community, where many prior video resources are either not publicly released or carry restrictive/unclear licenses that hinder reuse and benchmarking. Our annotations are released under the same CC BY-NC-SA 4.0 license as OpenTTGames, allowing free non-commercial use, modification, and redistribution, with appropriate attribution.

Emil Hovad, Therese Hougaard-Jensen, Line Katrine Harder Clemmensen

Recent advances of deep learning makes it possible to identify specific events in videos with greater precision. This has great relevance in sports like tennis in order to e.g., automatically collect game statistics, or replay actions of specific interest for game strategy or player improvements. In this paper, we investigate the potential and the challenges of using deep learning to classify tennis actions. Three models of different size, all based on the deep learning architecture SlowFast were trained and evaluated on the academic tennis dataset THETIS. The best models achieve a generalization accuracy of 74 %, demonstrating a good performance for tennis action classification. We provide an error analysis for the best model and pinpoint directions for improvement of tennis datasets in general. We discuss the limitations of the data set, general limitations of current publicly available tennis data-sets, and future steps needed to make progress.