LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation

TL;DR

LaneTCA introduces a transformer-based approach that effectively aggregates long-term and short-term temporal context in video lane detection, significantly improving accuracy on benchmark datasets.

Contribution

The paper proposes novel accumulative and adjacent attention modules based on transformers to better utilize temporal context in video lane detection.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively combines long-term and short-term temporal information.

Demonstrates significant improvements over existing methods.

Abstract

In video lane detection, there are rich temporal contexts among successive frames, which is under-explored in existing lane detectors. In this work, we propose LaneTCA to bridge the individual video frames and explore how to effectively aggregate the temporal context. Technically, we develop an accumulative attention module and an adjacent attention module to abstract the long-term and short-term temporal context, respectively. The accumulative attention module continuously accumulates visual information during the journey of a vehicle, while the adjacent attention module propagates this lane information from the previous frame to the current frame. The two modules are meticulously designed based on the transformer architecture. Finally, these long-short context features are fused with the current frame features to predict the lane lines in the current frame. Extensive quantitative and qualitative experiments are conducted on two prevalent benchmark datasets. The results demonstrate the effectiveness of our method, achieving several new state-of-the-art records. The codes and models are available at https://github.com/Alex-1337/LaneTCA