Homography Estimation in Complex Topological Scenes
This addresses the need for robust extrinsic calibration in surveillance applications like traffic analysis and crime detection, offering an incremental improvement over existing methods.
The paper tackles the problem of automated camera re-calibration in surveillance videos under varying conditions by proposing a method using a Spatial Transformer Network and a novel topological loss function, resulting in an improvement of up to 12% in IoU metric compared to a state-of-the-art model across multiple datasets.
Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r.t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.