Simultaneous Object Detection, Tracking, and Event Recognition
This work addresses the problem of combining multiple vision tasks efficiently for applications in computer vision, though it appears incremental as it builds on existing components.
The paper tackles the integration of object detection, tracking, and event recognition by leveraging their common internal structure to enable multidirectional information flow, resulting in performance that exceeds isolated components with linear asymptotic complexity.
The common internal structure and algorithmic organization of object detection, detection-based tracking, and event recognition facilitates a general approach to integrating these three components. This supports multidirectional information flow between these components allowing object detection to influence tracking and event recognition and event recognition to influence tracking and object detection. The performance of the combination can exceed the performance of the components in isolation. This can be done with linear asymptotic complexity.