  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.
