Tracking – Jan-Nico Zaech

Optimizing Long-Term Player Tracking and Identification in NAO Robot Soccer by fusing Game-state and External Video

zaech — Fri, 02 Jun 2023 15:45:51 +0000

Giuliano Albanese*, Arka Mitra*, Jan-Nico Zaech*, Yupeng Zhao*, Ajad Chhatkuli, and Luc Van Gool

International Conference on Robotics and Automation Workshops, ICRA 2023 (CoPerception: Collaborative Perception and Learning)

Abstract

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking these games requires the handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification
pipeline. It fuses external non-calibrated camera data with the robots’ internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cups.

Adiabatic Quantum Computing for Multi Object Tracking

zaech — Sun, 19 Jun 2022 00:00:18 +0000

Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc Van Gool

Conference on Computer Vision and Pattern Recognition, CVPR 2022

Abstract

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution for this, as it has the potential to provide a considerable speedup on a range of NP-hard optimization problems in the near future. However, current MOT formulations are unsuitable for quantum computing due to their scaling properties. In this work, we therefore propose the first MOT formulation designed to be solved with AQC. We employ an Ising model that represents the quantum mechanical system implemented on the AQC. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers. Finally, we demonstrate that our MOT problem is already solvable on the current generation of real quantum computers for small examples, and analyze the properties of the measured solutions.

Learnable Online Graph Representations for 3D Multi-Object Tracking

zaech — Mon, 23 May 2022 16:41:28 +0000

Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Martin Danelljan, Luc Van Gool

International Conference on Robotics and Automation Workshops, ICRA 2022

Abstract

Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications
such as autonomous driving, robotics or augmented reality. Most recent approaches for 3D multi object tracking (MOT) from LIDAR use object dynamics together with a set of handcrafted features to match detections of objects. However, manually designing such features and heuristics is cumbersome and often leads to suboptimal performance. In this work, we instead strive towards a unified and learning based approach to the 3D MOT problem. We design a graph structure to jointly process detection and track states in an online manner. To this end, we employ a Neural Message Passing network for data association that is fully trainable. Our approach provides a natural way for track initialization and handling of false positive detections, while significantly improving track stability. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.