Spatiotemporal Deformable Models for Long-Term Complex Activity Detection

• URL: http://arxiv.org/abs/2104.08194v1
• Date: Fri, 16 Apr 2021 16:05:34 GMT
• Title: Spatiotemporal Deformable Models for Long-Term Complex Activity Detection
• Authors: Salman Khan and Fabio Cuzzolin
• Abstract summary: Long-term complex activity recognition can be crucial for autonomous systems such as cars and surgical robots. Most current methods are designed to merely localise short-term action/activities or combinations of actions that only last for a few frames or seconds. Our framework consists of three main building blocks: (i) action detection, (ii) the modelling of the deformable geometry of parts, and (iii) a sparsity mechanism.
• Score: 23.880673582575856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term complex activity recognition and localisation can be crucial for the decision-making process of several autonomous systems, such as smart cars and surgical robots. Nonetheless, most current methods are designed to merely localise short-term action/activities or combinations of atomic actions that only last for a few frames or seconds. In this paper, we address the problem of long-term complex activity detection via a novel deformable, spatiotemporal parts-based model. Our framework consists of three main building blocks: (i) action tube detection, (ii) the modelling of the deformable geometry of parts, and (iii) a sparsity mechanism. Firstly, action tubes are detected in a series of snippets using an action tube detector. Next, a new 3D deformable RoI pooling layer is designed for learning the flexible, deformable geometry of the constellation of parts. Finally, a sparsity strategy differentiates between activated and deactivate features. We also provide temporal complex activity annotation for the recently released ROAD autonomous driving dataset and the SARAS-ESAD surgical action dataset, to validate our method and show the adaptability of our framework to different domains. As they both contain long videos portraying long-term activities they can be used as benchmarks for future work in this area.

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