Ensembling object detectors for image and video data analysis
This method aims to improve object detection performance and precision for researchers and practitioners working with image and video data, potentially aiding faster bounding box annotation.
This paper proposes a method for ensembling multiple object detectors to improve detection performance and bounding box precision on image data. It also extends this to video data using a two-stage tracking-based refinement scheme.
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We further extend it to video data by proposing a two-stage tracking-based scheme for detection refinement. The proposed method can be used as a standalone approach for improving object detection performance, or as a part of a framework for faster bounding box annotation in unseen datasets, assuming that the objects of interest are those present in some common public datasets.