  Temporal action detection aims to recognize the action category and determine
each action instance's starting and ending time in untrimmed videos. The mixed
methods have achieved remarkable performance by seamlessly merging anchor-based
and anchor-free approaches. Nonetheless, there are still two crucial issues
within the mixed framework: (1) Brute-force merging and handcrafted anchor
design hinder the substantial potential and practicality of the mixed methods.
(2) Within-category predictions show a significant abundance of false
positives. In this paper, we propose a novel Boundary Discretization and
Reliable Classification Network (BDRC-Net) that addresses the issues above 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,
eliminating the need for the traditional handcrafted anchor design.
Furthermore, the reliable classification module (RCM) predicts reliable global
action categories to reduce false positives. Extensive experiments conducted on
different benchmarks demonstrate that our proposed method achieves competitive
detection performance. The code will be released at
https://github.com/zhenyingfang/BDRC-Net.
