Group activity recognition is the task of understanding the activity
conducted by a group of people as a whole in a multi-person video. Existing
models for this task are often impractical in that they demand ground-truth
bounding box labels of actors even in testing or rely on off-the-shelf object
detectors. Motivated by this, we propose a novel model for group activity
recognition that depends neither on bounding box labels nor on object detector.
Our model based on Transformer localizes and encodes partial contexts of a
group activity by leveraging the attention mechanism, and represents a video
clip as a set of partial context embeddings. The embedding vectors are then
aggregated to form a single group representation that reflects the entire
context of an activity while capturing temporal evolution of each partial
context. Our method achieves outstanding performance on two benchmarks,
Volleyball and NBA datasets, surpassing not only the state of the art trained
with the same level of supervision, but also some of existing models relying on
stronger supervision.

本研究提出了一种新的基于 Transformer 模型的团队活动识别方法，该方法借助注意机制对部分环节进行定位与编码，再对其进行聚合以反映整个活动的完整背景和每个环节的时间演化，无需借助较强的监督方法，在两个基准测试中均表现良好。