Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
This addresses the problem of expensive labeled data and suboptimal tracking in MOT for computer vision applications, representing a novel approach but with incremental improvements in robustness.
The paper tackles the challenge of online Multi-Object Tracking (MOT) by proposing a Tracking-by-Animation framework that enables label-free and end-to-end learning, using reconstruction error for training and improving robustness with Reprioritized Attentive Tracking.
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However, the commonly used supervised learning approaches require the labeled data (e.g., bounding boxes), which is expensive for videos. Also, the TBD framework is usually suboptimal since it is not end-to-end, i.e., it considers the task as detection and tracking, but not jointly. To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames. Learning is then driven by the reconstruction error through backpropagation. We further propose a Reprioritized Attentive Tracking to improve the robustness of data association. Experiments conducted on both synthetic and real video datasets show the potential of the proposed model. Our project page is publicly available at: https://github.com/zhen-he/tracking-by-animation