Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering
This work addresses a specific challenge in computer vision for applications requiring robust object detection in videos with periodic motion, representing an incremental improvement.
The paper tackles the problem of object detection in videos where objects exhibit periodic motion by formalizing periodic OD and proposing a Gaussian Process-based filtering method to correct erroneous predictions. The approach improves detection performance significantly in simulations with various models and trajectories.
Object Detection (OD) is an important task in Computer Vision with many practical applications. For some use cases, OD must be done on videos, where the object of interest has a periodic motion. In this paper, we formalize the problem of periodic OD, which consists in improving the performance of an OD model in the specific case where the object of interest is repeating similar spatio-temporal trajectories with respect to the video frames. The proposed approach is based on training a Gaussian Process to model the periodic motion, and use it to filter out the erroneous predictions of the OD model. By simulating various OD models and periodic trajectories, we demonstrate that this filtering approach, which is entirely data-driven, improves the detection performance by a large margin.