ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box (2303.15334v1)

Abstract: Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitable changing of detection scores leads to object missing after tracking. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories. The simple and generic data association strategy shows effectiveness under both 2D and 3D settings. In 3D scenarios, it is much easier for the tracker to predict object velocities in the world coordinate. We propose a complementary motion prediction strategy that incorporates the detected velocities with a Kalman filter to address the problem of abrupt motion and short-term disappearing. ByteTrackV2 leads the nuScenes 3D MOT leaderboard in both camera (56.4% AMOTA) and LiDAR (70.1% AMOTA) modalities. Furthermore, it is nonparametric and can be integrated with various detectors, making it appealing in real applications. The source code is released at https://github.com/ifzhang/ByteTrack-V2.

• The paper introduces a hierarchical data association strategy that refines detection box utilization to reduce missed detections and fragmented trajectories.
• It employs complementary motion prediction with Kalman filtering to robustly track objects in challenging 3D scenarios.
• The approach achieves state-of-the-art performance on nuScenes with AMOTA scores up to 70.1% for LiDAR-based tracking.

ByteTrackV2: A Comprehensive Approach to Multi-Object Tracking

The paper "ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box" presents a nuanced approach to improving multi-object tracking (MOT) performance by addressing the challenges associated with tracking objects using detection boxes in both 2D and 3D scenarios. The authors propose methodologies that refine the process of associating detection boxes to maintain object identities across varying video frames, a notoriously challenging task considering the typical issues of object occlusion, disappearance, and fragmented trajectories.

Core Contributions

1. Hierarchical Data Association Strategy: The authors introduce a hierarchical data association strategy aimed at extracting true object instances from low-score detection boxes. This approach effectively mitigates the occurrences of missed detections and fragmented tracking paths, which are prevalent when relying solely on high-confidence boxes.
2. Complementary Motion Prediction: They propose a method that integrates detected object velocities with a Kalman filter to enhance the tracker's ability to predict abrupt object motion or deal with temporary occlusions. This is particularly beneficial in 3D tracking contexts where estimating object velocities in the world coordinate frame is more feasible.
3. Performance and Implementation: The proposed ByteTrackV2 model excels in the nuScenes 3D MOT leaderboard, achieving an average multi-object tracking accuracy (AMOTA) of 56.4% for camera-based tracking and 70.1% for LiDAR-based tracking. The nonparametric nature of ByteTrackV2 allows it to be implemented effortlessly with a variety of detectors, showcasing its practical utility in real-world applications.

Experimental Validation

The methodology undergoes rigorous experimental validation, leveraging diverse datasets and metrics to ensure robust performance across different tracking scenarios. The authors provide comprehensive comparisons against existing methods, underlining the efficacy of their data association and motion prediction strategies. The model's results on the nuScenes benchmark demonstrate its superior detection accuracy and track continuity, findings that reinforce the practical applicability of the approach.

Implications and Future Directions

The presented work has significant implications for the MOT field, notably improving the robustness of tracking systems in dynamic and cluttered environments. By enhancing the precision of object association and leveraging both 2D and 3D modalities, ByteTrackV2 stands to influence applications in autonomous driving, surveillance, and robotic navigation, where accurate and persistent object tracking is vital.

Future exploration may involve refining the hierarchical association strategy to further reduce computational overhead and enhance scalability in dense environments. Additionally, integrating ByteTrackV2 with emerging deep learning models and edge computing frameworks could expand its applicability in real-time systems.

In conclusion, ByteTrackV2 represents a substantive advancement in multi-object tracking technology by methodically addressing key challenges inherent in this domain. The blend of hierarchical association and motion prediction strategies sets a new standard in the efficiency and efficacy of tracking systems, providing a robust foundation for future developments in this field.