The paper introduces a new benchmark of 345 annotated tennis ball bounces and racket hits across eight video clips featuring diverse viewpoints, including broadcast, fixed elevated, phone, and court-level angles. It evaluates several event detection methods—supervised spotters, zero-shot vision-language models, and a parameter-free arbitration layer called RallyArb—showing that supervised models perform well on broadcast footage but poorly on non-broadcast views, while RallyArb maintains high bounce detection across all viewpoints but struggles with precise hit timing. The dataset, evaluation tools, and frozen predictions are publicly released to support further research.
arXiv:2609.28049v1 Announce Type: cross Abstract: Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as ev...
Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Ama...
arXiv:2607. 11548v1 Announce Type: cross Abstract: Spatial football metrics such as pitch control assume access to the positions of all 22 players, yet the most widely available source of positional data -- the broadcast main camera -- shows only 10-16 of them at any moment.
The paper introduces EventNet, a two‑stage pipeline that uses 2D keypoints of players, table corners, and the ball to detect key events in table tennis videos. First, a keypoint transformer condenses the pose and ball information into a robust representation; second, a transformer encoder predicts how close each frame is to the next and previous ball‑racket contact using a novel temporal cosine‑like target signal. Experiments on Latte‑MV and TTHQ datasets show high accuracy, with an F1 score of 91.16% and a mean frame deviation of 0.42 on Latte‑MV, and 73.08% / 1.16 on TTHQ.
arXiv:2610.31734v1 Announce Type: new Abstract: A live esports broadcast needs a director who chooses which camera goes on air and a caster who talks over that choice, every second and without knowin...
arXiv:2609.09528v1 Announce Type: new Abstract: Video large language models (Video-LLMs) are increasingly used as the perceptual front end of world models, a role that assumes they can read motion: h...
arXiv:2609.37938v1 Announce Type: cross Abstract: Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggr...
The paper evaluates event-triggered recording strategies for static surveillance cameras, comparing six motion-based triggers, a CNN detector, strided detection, and hybrid approaches on a benchmark of seven scenes. Detector-based triggers reduce idle frame storage to 19‑28% versus 36‑89% for motion triggers, and remain robust under camera jitter, keeping under 29% idle frames. In addition, the study shows that increasing YOLO11n input resolution improves detection accuracy more than larger models, and that longer track memory boosts tracking IDF1, though detection remains the limiting factor.
SurgStreamer is a system that proactively monitors surgical video streams by separating procedure‑specific perception—mapping a 1‑FPS video to a structured state of tools, actions, and phases—from procedure‑agnostic reasoning that evaluates formal monitoring conditions over this state and an event memory. In a sealed test set of 12 laparoscopic cholecystectomies, SurgStreamer achieved a macro event‑F1 of 0.598 over full procedures and 0.677 on 75‑second windows, outperforming streaming vision‑language models. Re‑training only the perception component improved held‑out performance from 0.239 to 0.470, and a controller trained on cholecystectomy data performed comparably to an in‑domain controller on pituitary surgery, highlighting perception under distribution shift as the main limitation.
arXiv:2610. 39676v1 Announce Type: cross Abstract: Event cameras offer microsecond temporal resolution, high dynamic range and no motion blur, but referring video object segmentation (RVOS) lacks event-stream datasets and models.
arXiv:2608. 06361v1 Announce Type: new Abstract: Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure modes hard to isolate.
arXiv:2610.29913v1 Announce Type: new Abstract: Thermal cameras can watch over older people living alone without capturing faces, and multimodal large language models (LLMs) can judge a short video w...