Abstract
We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.
Highlights
As one of the most interesting aspects of multimedia content analysis, the multimedia event detection (MED) is becoming an important research area for computer vision in recent years
According to the definition by the National Institute of Standards and Technology (NIST) [1], an event (1) is a complex activity occurring at a specific place and time, (2) involves people interacting with other people and/or objects, (3) consists of a number of human actions, processes, and activities that are loosely or tightly organized and that have significant temporal and semantic relationships to the overarching activity, and (4) is directly observable
In the experiment and analysis section, we show that our 1000OBK is a robust event descriptor which achieves better performances than the state-of-the-art approaches on the TRECVID MED 2012 dataset
Summary
As one of the most interesting aspects of multimedia content analysis, the multimedia event detection (MED) is becoming an important research area for computer vision in recent years. Most of the current researches are focused on specific areas, such as sports video [2], news video [3], and surveillance video [4] These approaches do not perform well when used for the online or web based event detection due to two types of issues, which are the secure access control issue and the large scale robust representation issue. The classification results for selected events and video clips were shown graphically in our web system
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