Cross-View Referring Multi-Object Tracking (2412.17807v1)

Abstract: Referring Multi-Object Tracking (RMOT) is an important topic in the current tracking field. Its task form is to guide the tracker to track objects that match the language description. Current research mainly focuses on referring multi-object tracking under single-view, which refers to a view sequence or multiple unrelated view sequences. However, in the single-view, some appearances of objects are easily invisible, resulting in incorrect matching of objects with the language description. In this work, we propose a new task, called Cross-view Referring Multi-Object Tracking (CRMOT). It introduces the cross-view to obtain the appearances of objects from multiple views, avoiding the problem of the invisible appearances of objects in RMOT task. CRMOT is a more challenging task of accurately tracking the objects that match the language description and maintaining the identity consistency of objects in each cross-view. To advance CRMOT task, we construct a cross-view referring multi-object tracking benchmark based on CAMPUS and DIVOTrack datasets, named CRTrack. Specifically, it provides 13 different scenes and 221 language descriptions. Furthermore, we propose an end-to-end cross-view referring multi-object tracking method, named CRTracker. Extensive experiments on the CRTrack benchmark verify the effectiveness of our method. The dataset and code are available at https://github.com/chen-si-jia/CRMOT.

• The paper introduces Cross-View Referring Multi-Object Tracking (CRMOT), a novel task leveraging multiple camera views to improve object tracking reliability by overcoming single-view limitations like occlusions.
• Authors propose the CRTracker framework, an end-to-end model designed for CRMOT that integrates vision and language processing with a prediction module for enhanced accuracy.
• Experiments demonstrate CRTracker achieves significant performance gains on the new CRTrack benchmark, notably improving CVRIDF1 by 31.45% over best single-view methods.

Cross-View Referring Multi-Object Tracking: Advances and Challenges

The paper "Cross-View Referring Multi-Object Tracking" introduces a novel task within the computer vision domain that addresses the limitations inherent in existing Referring Multi-Object Tracking (RMOT) systems. The RMOT focuses on tracking multiple objects in a scene that align with specified language descriptions, but current approaches predominantly operate in a single-view framework. This paper proposes an innovative extension to this model—Cross-view Referring Multi-Object Tracking (CRMOT)—which leverages multiple overlapping camera views to improve object visibility and tracking accuracy.

Core Contributions

The authors identify several key contributions of their work:

1. Introduction of CRMOT: The CRMOT task enhances traditional RMOT by utilizing cross-view camera systems to mitigate issues such as occlusions and incomplete object visibility that are pervasive in single-view scenarios. By incorporating multiple views, CRMOT systems can more reliably maintain object identity and improve the accuracy of tracking results that correspond to detailed language descriptions.
2. Development of CRTrack Benchmark: To evaluate CRMOT systems, the authors introduce CRTrack, a comprehensive benchmark comprising 13 diverse scenes, with 82,000 frames, 344 distinct objects, and 221 language descriptions sourced from the DIVOTrack and CAMPUS datasets. This benchmark facilitates a robust evaluation of CRMOT capabilities across varying conditions and complexities.
3. Proposal of CRTracker Framework: The paper describes the CRTracker, an end-to-end model designed specifically for the CRMOT task. CRTracker capitalizes on both vision and language processing advancements by integrating tracking capabilities from the CrossMOT and multi-modal potential from the APTM framework. Additionally, a prediction module enhances tracking accuracy by evaluating fused scores from frame-to-frame associations.

Experimental Results

Extensive experiments conducted on the CRTrack benchmark demonstrate the potential of CRTracker over existing methods adapted from RMOT for cross-view applications. Notably, CRTracker achieves a CVRIDF1 improvement of 31.45% and a CVRMA increase of 25.83% over the best performing single-view RMOT methods in the in-domain evaluations. Even in challenging cross-domain scenarios, CRTracker retains a significant performance edge, underscoring its generalization capabilities across unseen domains.

Implications and Future Directions

The introduction of CRMOT holds significant implications for fields reliant on robust multi-object tracking. Theoretical advancements in cross-view tracking could lead to practical enhancements in surveillance systems, autonomous vehicles, and any applications requiring coherent integration of visual data across multiple inputs. Future research could explore deeper integration with advanced LLMs to improve the semantic understanding of language descriptions and further refine object tracking accuracy.

The CRMOT task and the CRTrack benchmark present new challenges and opportunities for the vision-language integration—suggesting that further research could lead to more sophisticated multimodal systems. The ongoing evolution of cross-view tracking frameworks like the CRTracker could significantly influence both theoretical inquiries and practical implementations in artificial intelligence and computer vision.

In summary, this research represents a substantial step forward in addressing limitations in RMOT by developing a cross-view framework. By doing so, it opens pathways for future investigations in multi-object tracking that leverage the robust combination of multi-modal data inputs, promising more reliable and accurate tracking outcomes in complex environments.