RobStruck: Improving occlusion handling of structured tracking-by-detection using robust Kalman filter

Starting from an object's location in a video frame, tracking-by-detection methods find the location of that object in a subsequent video frame. The tracker's detection step may produce multiple false positives during short-term occlusions, which can result in loss of track. We propose a tracking-by-detection method that is robust to short-term occlusions and false positives. Here, we extend the Struck tracker, which is based on structured SVM, to output bounding boxes at multiple scales. In addition to predicting scale changes, we use the Robust Kalman filter to decrease false-positive detections and to increase the tracker resilience to short-time occlusions. Here, we develop a special strategy for the tracker's update step, which is designed to decrease over fitting and to allow for the tracker to recover from loss of track. We thoroughly evaluate our method on a publicly available video dataset and show that it outperforms the state-of-the-art.