Extending Generic BPM with Computer Vision Capabilities

Leveraging Business Process Management (BPM) is key to enabling business agility in organizations. Video analysis is a nascent technology that allows for innovative sensing and understanding of a large family of tasks across many diverse application domains. This includes interactions between persons, objects, and the environment in domains such as Healthcare and Retail, as well as more general activities (e.g., in video-surveillance or Transportation). It can, therefore, enable better BPM by giving the opportunity to augment, complement, and improve the observation, description, monitoring, triggering, and execution of a broad array of tasks, including new ones that can only be described visually. This may be of particular interests in cyber-physical systems where interactions between human agents and artificial agents can be tracked and managed in the context of various business processes. This paper proposes a way to integrate video data and analysis into the control flow of business processes, in a way that enables the seamless augmentation of business process execution with information from the observable environment.

[1]  Rémi Ronfard,et al.  A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..

[2]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Samy Bengio,et al.  Large-Scale Object Classification Using Label Relation Graphs , 2014, ECCV.

[4]  Frank Budinsky,et al.  Eclipse Modeling Framework , 2003 .

[5]  Dongsoo Kim,et al.  Context-Aware Business Process Management for Personalized Healthcare Services , 2013, 2013 IEEE International Conference on Services Computing.

[6]  Jason J. Corso,et al.  Action bank: A high-level representation of activity in video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Cordelia Schmid,et al.  Activity representation with motion hierarchies , 2013, International Journal of Computer Vision.

[8]  Deva Ramanan,et al.  Parsing Videos of Actions with Segmental Grammars , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Richard Hull,et al.  Business Artifacts: A Data-centric Approach to Modeling Business Operations and Processes , 2009, IEEE Data Eng. Bull..

[10]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[11]  Fei-Fei Li,et al.  Learning latent temporal structure for complex event detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.

[13]  Silvio Savarese,et al.  Recognizing human actions by attributes , 2011, CVPR 2011.