MonoSORT3D: A Monocular Approach for Online Auxiliary-Free Multi-Object Tracking

Using a mono camera, in ADAS, as the primary sensor offers a significant advantage by minimizing system complexity since no extra fusion step is considered. This approach also leverages recent advancements in computer vision and deep learning, which enable high levels of environmental understanding and scene analysis from visual data alone. As such, mono-camera setups hold promise for achieving reliable perception at scale and lead to a growing interest in monocular approaches, particularly for detecting and tracking dynamic objects. However, traditional mono-camera methods are often dependent on auxiliary inputs from GPS or maps, which may be unreliable in complex terrain or areas with poor signal coverage. In this work, we introduce an effective monocular 3D multi-object tracking approach, called MonoSORT3D, which operates without requiring additional auxiliary inputs. Evaluation of our method on the KITTI and MOT17 datasets demonstrates the competitive performance against state-of-the-art methods. Additionally, we provide an in-depth analysis of MonoSORT3D architecture by conducting an ablation study on different components within.