SRI-Sarnoff AURORA System at TRECVID 2012 Multimedia Event Detection and Recounting.
Hui Cheng, Jingen Liu, Saad Ali, Omar Javed, Qian Yu, Amir Tamrakar, Ajay Divakaran, Harpreet Sawhney, Raghaven Manmatha, James W Allan, Alex G. Hauptmann, Mubarak A. Shah, Subhabrata Bhattacharya, Afshin Dehghan, Gerald H Friedland, Benjamin Elizalde, Trevor J. Darrell, Michael Witbrock, Jon Curtis · Journal of International Crisis and Risk Communication Research · 2012
In this paper, we describe the evaluation results for TRECVID 2012 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks as a part of SRI-Sarnoff AURORA system that is devel-oped under the IARPA ALDDIN program. In AURORA system, we incorporated various low-level fea-tures that capture color, appearance, motion, and audio information in videos. Based on these low-level features, we developed Fixed-Pattern and Object-Orientated spatial feature pooling, which result in signif-icant performance improvement to our system. In addition, we collected more than 1800 concepts and designed a set of concept pooling approaches to build the Concept Based Event Representation (CBER, i.e., high-level features). We submitted six runs exploring various fusions of low-level features, high-level features, and ASR/OCR features for MED task. All runs achieve satisfactory results. In particular, two EK10Ex runs for both pre-specified events (PS-Events) and ad-hoc events (AH-Events) obtain relatively better results. In MER task, we developed an approach to provide a breakdown of the evidences of why the MED decision has been made by exploring the SVM-based event detector. Furthermore, we designed evidence specific verification and detection to reduce uncertainty and improve key evidence discovery. Our MER evaluation results for MER-to-Event are very good.