Attention-Based Solutions for Occlusion Challenges in Person Tracking

Person tracking and association is a complex task in computer vision applications. Even with a powerful detector, a highly accurate association algorithm is necessary to match and track the correct person across all frames. This method has numerous applications in surveillance, and its complexity increases with the number of detected objects and their movements across frames. A significant challenge in person tracking is occlusion, which occurs when an individual being tracked is partially or fully blocked by another object or person. This can make it difficult for the tracking system to maintain the identity of the individual and track them effectively.

In this research, we propose a solution to the occlusion problem by utilizing an occlusion-aware spatial attention transformer. We have divided the entire tracking association process into two scenarios: occlusion and no-occlusion. When a detected person with a specific ID suddenly disappears from a frame for a certain period, we employ advanced methods such as Detector Integration and Pose Estimation to ensure the correct association. Additionally, we implement a spatial attention transformer to differentiate these occluded detections, transform them, and then match them with the correct individual using the Cosine Similarity Metric.

The features extracted from the attention transformer provide a robust baseline for detecting people, enhancing the algorithms adaptability and addressing key challenges associated with existing approaches. This improved method reduces the number of misidentifications and instances of ID switching while also enhancing tracking accuracy and precision.