This paper presents LAPA (Look Around and Pay Attention), a novel end-to-end transformer-based architecture for multi-camera point tracking that integrates appearancebased matching with geometric constraints. Traditional pipelines decouple detection, association, and tracking, leading to error propagation and temporal inconsistency in challenging scenarios. LAPA addresses these limitations by leveraging attention mechanisms to jointly reason across views and time, establishing soft correspondences through a cross-view attention mechanism enhanced with geometric priors. Instead of relying on classical triangulation, we construct 3D point representations via attention-weighted aggregation, inherently accommodating uncertainty and partial observations. Temporal consistency is further maintained through a transformer decoder that models long-range dependencies, preserving identities through extended occlusions. Extensive experiments on challenging datasets, including our newly created multi-camera (MC) versions of TAPVid-3D panoptic and PointOdyssey, demonstrate that our unified approach significantly outperforms existing methods, achieving 37.5% APD on TAPVid-3D-MC and 90.3% APD on PointOdyssey-MC, particularly excelling in scenarios with complex motions and occlusions. Code is available at https://github.com/ostadabbas/Look-Around-and-Pay-Attention-LAPA-.

This paper introduces MOTE (MOre Than meets the Eye), a novel multi-object tracking (MOT) algorithm designed to address the challenges of tracking occluded objects. By integrating deformable detection transformers with a custom disocclusion matrix, MOTE significantly enhances the ability to track objects even when they are temporarily hidden from view. The algorithm leverages optical flow to generate features that are processed through a softmax splatting layer, which aids in the creation of a disocclusion matrix. This matrix plays a crucial role in maintaining track consistency by estimating the motion of occluded objects. MOTE's architecture includes modifications to the enhanced track embedding module (ETEM), which allows it to incorporate these advanced features into the track query layer embeddings. This integration ensures that the model not only tracks visible objects but also accurately predicts the trajectories of occluded ones, much like the human visual system. The proposed method is evaluated on multiple datasets, including MOT17, MOT20, and DanceTrack, where it achieves impressive tracking metrics--82.0 MOTA and 66.3 HOTA on the MOT17 dataset, 81.7 MOTA and 65.8 HOTA on the MOT20 dataset, and 93.2 MOTA and 74.2 HOTA on the DanceTrack dataset. Notably, MOTE excels in reducing identity switches and maintaining consistent tracking in complex real-world scenarios with frequent occlusions, outperforming existing state-of-the-art methods across all tested benchmarks.

This paper introduces the dynamic robust adaptive graph-based tracker (DragonTrack), as a novel end-to-end framework for multi-person tracking (MPT) by integrating a detection transformer model for object detection and feature extraction with a graph convolutional network for re-identification. DragonTrack leverages encoded features from the transformer for precise subject matching and track maintenance, while the graphical component processes these features alongside geometric data to predict subsequent positions of tracked people. This methodology aims to enhance tracking accuracy and reliability, as evidenced by improvements in key metrics such as higher order tracking accuracy (HOTA) and multiple object tracking accuracy (MOTA). We quantitatively compare Drag-onTrack with state-of-the-art methods on MOT17, MOT20, and DanceTrack datasets, in which DragonTrack outperforms other methods. In challenging scenarios such as DanceTrack, DragonTrack achieves an impressive MOTA score of 93.4, significantly higher than the second-best SOTA method, ByteTrack, which achieves only 89.6. Similarly, on MOT17, DragonTrack scores 82.0 in MOTA, sur-passing the closest competitor with a score of 80.3. On MOT20, DragonTrack attains a HOTA score of 63.2, out-performing the next best method scoring 62.611The DragonTrack code is available at https://github.com/ostadabbas/DragonTrack. .