Group Activity Recognition (GAR), which aims to identify activities performed collectively in videos, has gained significant attention recently. Existing GAR datasets typically annotate only a single Group Activity (GA) instance per sample, carefully selected from original videos. This approach, while precise, diverges significantly from real-world contexts, which often involve multiple GA instances. Moreover, single word-level annotations are insufficient to encapsulate the complex semantic information in GA, thereby constraining the expansion and research of other GA-related tasks.
To mitigate these limitations, a research team led by Wang Yun-Hong (Beihang University, China) published their new research on 15 December 2024 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed FIFAWC, a novel dataset for GAR characterized by three notable distinctions:
In the research, they benchmark FIFAWC on two tasks: traditional GAR and innovative GA video captioning. For GAR, they evaluate the classical detector-based approach ARG, and the state-of-the-art detector-free DFWSGAR. The results in Table 2 reveal high accuracy at category level, but low accuracy at sample level because of multiple GAs per sample, reflecting the complexity and challenge of FIFAWC. The assessment of the traditional captioning method PDVC and the Large Language Model-based VTimeLLM in GA video captioning is listed in Table 3. Compared to the exemplary performance (25.87 in terms of CIDEr) of PDVC on the ActivityNet dataset, the poor performance on FIFAWC indicates that further research is necessary for GA video captioning.
DOI: 10.1007/s11704-024-40027-3
Frontiers of Computer Science
Experimental study
Not applicable
FIFAWC: A Dataset with Detailed Annotation and Rich Semantics for Group Activity Recognition
15-Dec-2024