Temporal action detection aims to recognize the action category and determine
the starting and ending time of each action instance in untrimmed videos. The
mixed methods have achieved remarkable performance by simply merging
anchor-based and anchor-free approaches. However, there are still two crucial
issues in the mixed framework: (1) Brute-force merging and handcrafted anchors
design affect the performance and practical application of the mixed methods.
(2) A large number of false positives in action category predictions further
impact the detection performance. In this paper, we propose a novel Boundary
Discretization and Reliable Classification Network (BDRC-Net) that addresses
the above issues by introducing boundary discretization and reliable
classification modules. Specifically, the boundary discretization module (BDM)
elegantly merges anchor-based and anchor-free approaches in the form of
boundary discretization, avoiding the handcrafted anchors design required by
traditional mixed methods. Furthermore, the reliable classification module
(RCM) predicts reliable action categories to reduce false positives in action
category predictions. Extensive experiments conducted on different benchmarks
demonstrate that our proposed method achieves favorable performance compared
with the state-of-the-art. For example, BDRC-Net hits an average mAP of 68.6%
on THUMOS'14, outperforming the previous best by 1.5%. The code will be
released at this https URL

混合方法的时空动作检测在融合基于锚点和不基于锚点的方法上取得了显著性能，但仍然存在两个关键问题：蛮力融合和手工设计的锚点影响了混合方法的性能和实际应用，以及动作类别预测中大量的误报影响了检测性能。本文提出了一种新颖的边界离散和可靠分类网络（BDRC-Net），通过引入边界离散和可靠分类模块来解决上述问题。具体而言，边界离散模块（BDM）以边界离散的形式巧妙地融合了基于锚点和不基于锚点的方法，避免了传统混合方法所需的手工设计锚点。此外，可靠分类模块（RCM）预测可靠的动作类别以减少动作类别预测中的误报。在不同的基准测试上进行的大量实验证明，我们提出的方法与最先进的方法相比具有较好的性能。例如，在 THUMOS'14 上，BDRC-Net 的平均 mAP 达到了 68.6%，超过了先前最佳水平 1.5%。代码将在该链接处公开发布。