Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors

Soares, João; Shah, Avijit; Biswas, Topojoy

doi:10.48550/arxiv.2205.10450

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…E2E-Spot places second in the (concurrent) 2022 competition (within 1.1 avg-mAP), after Soares et al [54], due to the latter's strong performance on unshown actions (not visible in the frame). Soares et al [54,55] and Zhou et al [75] are two-phase approaches, combining pre-extracted features from multiple (5 to 6) heterogeneous, fine-tuned feature extractors and proposing downstream architectures and losses on those features. In contrast, E2E-Spot shows that direct, end-to-end training of a simple and compact model can be a surprisingly strong baseline.…”

Section: Results On the Soccernet Action Spotting Challengementioning

confidence: 99%

Spotting Temporally Precise, Fine-Grained Events in Video

Hong¹,

Zhang²,

Gharbi³

et al. 2022

Preprint

View full text Add to dashboard Cite

We introduce the task of spotting temporally precise, finegrained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason globally about the full-time scale of actions and locally to identify subtle frame-to-frame appearance and motion differences that identify events during these actions. Surprisingly, we find that top performing solutions to prior video understanding tasks such as action detection and segmentation do not simultaneously meet both requirements. In response, we propose E2E-Spot, a compact, end-to-end model that performs well on the precise spotting task and can be trained quickly on a single GPU. We demonstrate that E2E-Spot significantly outperforms recent baselines adapted from the video action detection, segmentation, and spotting literature to the precise spotting task. Finally, we contribute new annotations and splits to several fine-grained sports action datasets to make these datasets suitable for future work on precise spotting.

show abstract

Section: Results On the Soccernet Action Spotting Challengementioning

confidence: 99%