EventNet is a large-scale video corpus and event ontology consisting of 500 events associated with event-specific concepts. In order to improve the quality of the current EventNet, we conduct the following steps and introduce EventNet version 1.1: (1) manually verify the correctness of event labels for all videos; (2) remove the YouTube user bias by limiting the maximum number of videos in each event from the same YouTube user as 3; (3) remove the videos which are currently not accessible online; (4) remove the video belonging to multiple event categories. After the above procedure, some events may contain only a small number of videos, and therefore we crawl more videos for those events to ensure every event will contain more than 50 videos. Finally, EventNet version 1.1 contains 67,641 videos, 500 events, and 5,028 event-specific concepts. In addition, we train a Convolutional Neural Network (CNN) model for event classification via fine-tuning AlexNet using EventNet version 1.1. Then we use the trained CNN model to extract FC7 layer feature and train binary classifiers using linear SVM for each event-specific concept. We believe this new version of EventNet will significantly facilitate research in computer vision and multimedia, and will put it online for public downloading in the future.
[1]
Dong Liu,et al.
EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video
,
2015,
ACM Multimedia.
[2]
Bernard Ghanem,et al.
ActivityNet: A large-scale video benchmark for human activity understanding
,
2015,
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3]
Chih-Jen Lin,et al.
LIBSVM: A library for support vector machines
,
2011,
TIST.
[4]
Geoffrey E. Hinton,et al.
ImageNet classification with deep convolutional neural networks
,
2012,
Commun. ACM.
[5]
Li Fei-Fei,et al.
ImageNet: A large-scale hierarchical image database
,
2009,
CVPR.