Recognition of human actions and associated interactions with objects and the environment is an important problem in computer vision due to its potential applications in a variety of domains. Recently, graph convolutional networks that extract features from the skeleton have demonstrated promising performance. In this paper, we propose a novel Spatio-Temporal Pyramid Graph Convolutional Network (ST-PGN) for online action recognition for ergonomics risk assessment that enables the use of features from all levels of the skeleton feature hierarchy. The proposed algorithm outperforms state-of-art action recognition algorithms tested on two public benchmark datasets typically used for postural assessment (TUM and UW-IOM). We also introduce a pipeline to enhance postural assessment methods with online action recognition techniques. Finally, the proposed algorithm is integrated with a traditional ergonomics risk index (REBA) to demonstrate the potential value for assessment of musculoskeletal disorders in occupational safety.

This paper examines the problem of dynamic traffic scene classification under space-time variations in viewpoint that arise from video captured on-board a moving vehicle. Solutions to this problem are important for realization of effective driving assistance technologies required to interpret or predict road user behavior. Currently, dynamic traffic scene classification has not been adequately addressed due to a lack of benchmark datasets that consider spatiotemporal evolution of traffic scenes resulting from a vehicle's ego-motion. This paper has three main contributions. First, an annotated dataset is released to enable dynamic scene classification that includes 80 hours of diverse high quality driving video data clips collected in the San Francisco Bay area. The dataset includes temporal annotations for road places, road types, weather, and road surface conditions. Second, we introduce novel and baseline algorithms that utilize semantic context and temporal nature of the dataset for dynamic classification of road scenes. Finally, we showcase algorithms and experimental results that highlight how extracted features from scene classification serve as strong priors and help with tactical driver behavior understanding. The results show significant improvement from previously reported driving behavior detection baselines in the literature.

Holistic driving scene understanding is a critical step toward intelligent transportation systems. It involves different levels of analysis, interpretation, reasoning and decision making. In this paper, we propose a 3D dynamic scene analysis framework as the first step toward driving scene understanding. Specifically, given a sequence of synchronized 2D and 3D sensory data, the framework systematically integrates different perception modules to obtain 3D position, orientation, velocity and category of traffic participants and the ego car in a reconstructed 3D semantically labeled traffic scene. We implement this framework and demonstrate the effectiveness in challenging urban driving scenarios. The proposed framework builds a foundation for higher level driving scene understanding problems such as intention and motion prediction of surrounding entities, ego motion planning, and decision making.