Center point-based feature representation for tracking

Center points are commonly the results of anchor-free object detectors. Starting from this initial representation, a regression scheme is utilized to determine a target point set to capture object properties such as enclosing bounding boxes and further attributes such as class labels. When only trained for the detection tasks, the encoded center point feature representations are not well suited for tracking objects since the embedded features are not stable over time. To tackle this problem, we present an approach of joint detection and feature embedding for multiple object tracking. The proposed approach applies an anchor-free detection model to pairs of images to extract single-point feature representations. To generate temporal stable features which are suitable for track association across short time intervals, auxiliary losses are applied to reduce the distance of tracked identities in the embedded feature space. The abilities of the presented approach are demonstrated on real-world data reflecting prototypical object tracking scenarios.