Simple Cues Lead to a Strong Multi-Object Tracker (2206.04656v7)

Abstract: For a long time, the most common paradigm in Multi-Object Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For association, most models resourced to motion and appearance cues, e.g., re-identification networks. Recent approaches based on attention propose to learn the cues in a data-driven manner, showing impressive results. In this paper, we ask ourselves whether simple good old TbD methods are also capable of achieving the performance of end-to-end models. To this end, we propose two key ingredients that allow a standard re-identification network to excel at appearance-based tracking. We extensively analyse its failure cases, and show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-of-the-art performance. https://github.com/dvl-tum/GHOST.

• The paper revisits tracking-by-detection by integrating refined re-identification with on-the-fly domain adaptation and a tuned motion model to enhance MOT performance.
• The paper demonstrates that a well-tuned linear motion model effectively captures short-term object dynamics while reducing computational complexity.
• The paper validates its approach on multiple benchmarks, achieving superior IDF1 and HOTA scores without requiring extensive training on tracking datasets.

Multi-Object Tracking with Simple Cues

The discussed paper, "Simple Cues Lead to a Strong Multi-Object Tracker," revisits the established paradigm of tracking-by-detection (TbD) in the field of Multi-Object Tracking (MOT). The TbD paradigm traditionally involves the sequential processes of detecting objects in video frames and then associating these detections to form trajectories. While recent advancements in MOT have shifted towards complex learning-based methodologies, particularly those employing Transformer architectures, the authors question whether traditional, simpler tracking mechanisms can still deliver competitive results.

Core Contributions

1. Revisiting TbD with Refined Techniques: The authors critically evaluate the traditional TbD approach, questioning the complexity introduced by newer attention-based methods. They propose two significant modifications to enhance the TbD performance: an improved re-identification (reID) model and a complementary simple motion model. These improvements are designed to address the challenges and performance gaps often encountered in tracking scenarios with occlusions or camera movements.
2. Domain Adaptation for ReID Models: Recognizing the limitations of reID models when applied across different domains or datasets, the paper introduces an on-the-fly domain adaptation strategy. By customizing Batch Normalization (BN) statistics to the target domain sequences, this adaptation significantly improves the robustness and consistency of the appearance-based matching over varied datasets.
3. Innovative Use of Simple Motion Models: Despite the advancements in motion modeling within the MOT community, the paper demonstrates that a simple linear motion model, when well-tuned, suffices for capturing short-term object motions across video frames. This model simplifies the computational overhead while effectively maintaining tracking performance.

Empirical Evaluation

The effectiveness of the proposed methods is evaluated across four benchmark datasets: MOT17, MOT20, BDD100k, and DanceTrack. The tracker, named GHOST (Good Old Hungarian Simple Tracker), exhibits state-of-the-art results without being trained specifically on any tracking dataset. Notably, GHOST achieves superior IDF1 and HOTA performance metrics, which underline the balance of identity preservation and object coverage achieved by the model.

Theoretical and Practical Implications

• Simplicity and Interpretability: By demonstrating strong performance with a simplified approach, the paper casts doubt on the necessity of overly complex methods for all MOT tasks. This challenges the community to reconsider the adoption of highly intricate models when addressing standard tracking scenarios.
• Generalizability and Efficiency: The generalizability of GHOST across multiple diverse datasets highlights its robustness and offers a practical solution potentially deployable in resource-constrained environments where computational resources or large-scale annotated datasets are scarce.
• Encouraging Domain-Specific Insights: The holistic analysis presented in the paper provides valuable insights into the conditions where motion and appearance models excel or falter. This knowledge empowers researchers to refine their strategies based on specific application needs and tracking conditions.

Future Developments

The paper opens avenues for integrating these insights into more sophisticated models, promoting future research to further exploit the balance between data-driven learning and domain-specific priors. By prioritizing interpretability and performance, future work can build upon the foundations laid by this research to push the boundaries of MOT.

In conclusion, "Simple Cues Lead to a Strong Multi-Object Tracker" strengthens the argument for simplicity in complex tasks like MOT, encouraging a reassessment of current trends towards algorithmic complexity in favor of models that prioritize efficiency and robustness.