arXiv:2606.23604v2 Announce Type: replace-cross Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation. However, these descriptors are frame-independent, limiting their robustness as visual cues. Since such descriptors are often obtained from computationally intensive pretrained backbones, real-time MOT systems frequently abandon appearance cues altogether and rely solely on motion prediction and geometric association. In this work, we introduce Polycepta, an object-centric appearance state e
The increasing demand for robust real-time multi-object tracking in autonomous systems and surveillance drives innovation in more efficient and effective appearance estimation.
Improved object-centric appearance estimation enhances the reliability and performance of tracking systems, critical for widespread adoption of AI in various real-world applications.
Multi-object tracking systems can become more accurate and less reliant on computationally intensive static appearance descriptors, potentially enabling broader deployment in real-time edge devices.
- · Autonomous vehicle developers
- · Surveillance technology providers
- · Robotics integrators
- · AI hardware manufacturers
- · Providers of inefficient legacy tracking algorithms
- · Systems heavily reliant on cloud-based intensive appearance processing
More accurate and efficient multi-object tracking becomes feasible across a wider range of applications.
This could accelerate the development and deployment of autonomous systems and smart infrastructure by improving their perception capabilities.
Enhanced tracking precision might lead to new regulatory frameworks for autonomous systems and ethical considerations for widespread surveillance technologies.
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Read at arXiv cs.AI