Video-based complex human event recognition with a probabilistic transformer

Hongji Guo; Alexander Aved; Collen Roller; Erika Ardiles-Cruz; Qiang Ji

doi:10.1117/12.2664106

15 June 2023 Video-based complex human event recognition with a probabilistic transformer

Hongji Guo, Alexander Aved, Collen Roller, Erika Ardiles-Cruz, Qiang Ji

Proceedings Volume 12525, Geospatial Informatics XIII ; 125250J (2023) https://doi.org/10.1117/12.2664106
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Complex human events are high-level human activities that are composed of a set of interacting primitive human actions over time. Complex human event recognition is important for many applications, including security surveillance, healthcare, sports and games. Complex human event recognition requires recognizing not only the constituent primitive actions but also, more importantly, their long range spatiotemporal interactions. To meet this requirement, we propose to exploit the self-attention mechanism in the Transformer to model and capture the long-range interactions among primitive actions. We further extend the conventional Transformer to a probabilistic Transformer in order to quantify the event recognition confidence and to detect anomaly events. Specifically, given a sequence of human 3D skeletons, the proposed model first performs primitive action localization and recognition. The recognized primitive human actions and their features are then fed into the probabilistic Transformer for complex human event recognition. By using a probabilistic attention score, the probabilistic Transformer can not only recognize complex events but also quantify its prediction uncertainty. Using the prediction uncertainty, we further propose to detect anomaly events in an unsupervised manner. We evaluate the proposed probabilistic Transformer on FineDiving dataset and Olympics Sports dataset for both complex event recognition and abnormal event detection. The dataset consists of complex events composed of primitive diving actions. The experimental results demonstrate the effectiveness and superiority of our method against baseline methods.

Citation Download Citation

Hongji Guo, Alexander Aved, Collen Roller, Erika Ardiles-Cruz, and Qiang Ji "Video-based complex human event recognition with a probabilistic transformer", Proc. SPIE 12525, Geospatial Informatics XIII , 125250J (15 June 2023); https://doi.org/10.1117/12.2664106

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