Chapter 10 - Long-term deep object tracking

Abstract

Visual object tracking is one of the oldest tasks in computer vision. Interestingly, when it comes to longer sequences object tracking must address significant challenges that relate to model decay, that is the worsening of the model due to added bias, and target disappearance and reappearance. While model decay is also present in short-term tracking, in longer sequences the gradually added bias becomes so much more significant, that the tracker fails completely. The success of deep learning has influenced visual object tracking, especially in the context of long-term sequences. The reason is that with the commonly-used deep Siamese tracker design, one can relay all appearance comparisons to an offline learning of a similarity function. The offline learning of the Siamese trackers, in turn, eliminates model decay. However, eliminating model decay comes at the cost of possibly missing the target object in cases where the appearance of the target changes significantly compared to the first frame. For this reason, Siamese tracker variants with built-in invariances and equivariances are also proposed, allowing for adapting to appearance variations without exacerbating model decay. All considering, Siamese trackers perform well and are recommended in long-term visual object tracking.