TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video
Jinyuan Qu, Hongyang Li, Shilong Liu, Tianhe Ren, Zhaoyang Zeng, Lei Zhang
2024-12-03
Summary
This paper introduces TAPTRv3, an improved method for tracking points in long videos, which enhances the ability to follow specific points over time.
What's the problem?
Tracking points in long videos can be challenging because the points can change a lot as the video progresses. Existing methods often struggle to maintain accuracy, especially when there are sudden changes in the scene or when the video is very long. This can lead to errors in tracking and make it difficult to keep up with the moving points.
What's the solution?
TAPTRv3 improves upon its predecessor, TAPTRv2, by using both spatial and temporal context to enhance tracking accuracy. It introduces two new techniques: Context-aware Cross-Attention (CCA), which helps the model focus on relevant surrounding features when tracking a point, and Visibility-aware Long-Temporal Attention (VLTA), which considers how visible a point is over time to reduce errors caused by drifting. These innovations allow TAPTRv3 to track points more effectively, even in challenging conditions like scene cuts or rapid movements.
Why it matters?
This research is significant because it enhances the ability of AI systems to accurately track objects in long videos, which is important for applications like surveillance, sports analysis, and autonomous driving. By improving tracking technology, TAPTRv3 can help create more reliable systems that better understand and analyze video content.
Abstract
In this paper, we present TAPTRv3, which is built upon TAPTRv2 to improve its point tracking robustness in long videos. TAPTRv2 is a simple DETR-like framework that can accurately track any point in real-world videos without requiring cost-volume. TAPTRv3 improves TAPTRv2 by addressing its shortage in querying high quality features from long videos, where the target tracking points normally undergo increasing variation over time. In TAPTRv3, we propose to utilize both spatial and temporal context to bring better feature querying along the spatial and temporal dimensions for more robust tracking in long videos. For better spatial feature querying, we present Context-aware Cross-Attention (CCA), which leverages surrounding spatial context to enhance the quality of attention scores when querying image features. For better temporal feature querying, we introduce Visibility-aware Long-Temporal Attention (VLTA) to conduct temporal attention to all past frames while considering their corresponding visibilities, which effectively addresses the feature drifting problem in TAPTRv2 brought by its RNN-like long-temporal modeling. TAPTRv3 surpasses TAPTRv2 by a large margin on most of the challenging datasets and obtains state-of-the-art performance. Even when compared with methods trained with large-scale extra internal data, TAPTRv3 is still competitive.