A Discriminative CNN Video Representation for Event Detection

TL;DR

This paper introduces a novel CNN-based video representation using latent concept descriptors and advanced encoding, significantly improving event detection accuracy on large datasets with limited hardware.

Contribution

It proposes a new encoding method and latent concept descriptors for CNN features, achieving state-of-the-art event detection performance.

Findings

Improved mAP from 27.6% to 36.8% on TRECVID MEDTest 14

Enhanced performance over Dense Trajectories

Achieved top results in TRECVID MED 2014 competition

Abstract

In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus of this paper is to effectively leverage deep Convolutional Neural Networks (CNNs) to advance event detection, where only frame level static descriptors can be extracted by the existing CNN toolkit. This paper makes two contributions to the inference of CNN video representation. First, while average pooling and max pooling have long been the standard approaches to aggregating frame level static features, we show that performance can be significantly improved by taking advantage of an appropriate encoding method. Second, we propose using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable. The integration of the two contributions results in a new state-of-the-art performance in event detection over the largest video datasets. Compared to improved Dense Trajectories, which has been recognized as the best video representation for event detection, our new representation improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset. This work is the core part of the winning solution of our CMU-Informedia team in TRECVID MED 2014 competition.