Existing approaches for spatio-temporal action detection in videos are
limited by the spatial extent and temporal duration of the actions. In this
paper, we present a modular system for spatio-temporal action detection in
untrimmed security videos. We propose a two stage approach. The first stage
generates dense spatio-temporal proposals using hierarchical clustering and
temporal jittering techniques on frame-wise object detections. The second stage
is a Temporal Refinement I3D (TRI-3D) network that performs action
classification and temporal refinement on the generated proposals. The object
detection-based proposal generation step helps in detecting actions occurring
in a small spatial region of a video frame, while temporal jittering and
refinement helps in detecting actions of variable lengths. Experimental results
on the spatio-temporal action detection dataset - DIVA - show the effectiveness
of our system. For comparison, the performance of our system is also evaluated
on the THUMOS14 temporal action detection dataset.

本文提出了一种基于对象检测的两阶段模块化系统，用于在未裁剪的安全视频中进行时空动作检测，第一阶段使用层次聚类和时间抖动技术生成稠密的时空建议，第二阶段是一个 TRI-3D 网络，在生成的提议上执行动作分类和时态细化。实验结果表明本文提出的方法在区分不同时长、空间尺度范围内的动作检测方面效果显著。

基于提议的未修剪视频时空动作检测解决方案

A Proposal-Based Solution to Spatio-Temporal Action Detection in  Untrimmed Videos

This paper presents our contribution to the ChaLearn Challenge 2015 on
Cultural Event Classification. The challenge in this task is to automatically
classify images from 50 different cultural events. Our solution is based on the
combination of visual features extracted from convolutional neural networks
with temporal information using a hierarchical classifier scheme. We extract
visual features from the last three fully connected layers of both CaffeNet
(pretrained with ImageNet) and our fine tuned version for the ChaLearn
challenge. We propose a late fusion strategy that trains a separate low-level
SVM on each of the extracted neural codes. The class predictions of the
low-level SVMs form the input to a higher level SVM, which gives the final
event scores. We achieve our best result by adding a temporal refinement step
into our classification scheme, which is applied directly to the output of each
low-level SVM. Our approach penalizes high classification scores based on
visual features when their time stamp does not match well an event-specific
temporal distribution learned from the training and validation data. Our system
achieved the second best result in the ChaLearn Challenge 2015 on Cultural
Event Classification with a mean average precision of 0.767 on the test set.

本文提出了一种基于卷积神经网络的视觉特征和时间信息的分层分类器方案，并通过一个后期融合策略和时间修正步骤，得到了良好的文化事件分类结果，其中包括 2015 年 ChaLearn Challenge 的第二好表现。