DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.Comment: accepted at CVPR 202

Detection-aware multi-object tracking evaluation

Master Universitario en Deep Learning for Audio and Video Signal ProcessingMulti-Object Tracking (MOT) is a hot topic in the computer vision field. It is a complex task that requires a detector, to identify objects, and a tracker, to follow them. It is useful for self-driving, surveillance and robot vision, between others, where research teams and companies are trying to improve their models. In order to determine which model performs better, they are scored using tracking metrics. In this thesis we experiment with MOT metrics aware of detection by using correlation matrices. By analyzing the results, we realize that tracking metrics incur in certain issues that prevent them for correctly reflecting tracking performance. The performance of the detector is relevant when scoring tracking models. The problem observed is that tracking metrics weigh differently elements that evaluate detection performance. Thus, improving one detector’s aspect with a high weight in the MOT metric will significantly improve the tracker’s score, but not necessarily indicating the amount of effort done by the tracker. That is, trackers are not evaluated in a balanced way. In order to solve this issue with the tracker scoring, we present a new multi-object tracking metric, based on the effort done by the tracker given a certain set of detections. This effort is calculated based on the improvement of bounding boxes over the ones given by the detector and the precision to keep the trace of the objects in a sequence. The metric has been tested for two widely employed datasets and shows us its reliability scoring tracking metrics. Also, it do not incur in the problem presented above