Sequential Max-Margin Event Detectors

Abstract

Many applications in computer vision (e.g., games, human computer interaction) require a reliable and early detector of visual events. Existing event detection methods rely on one-versus-all or multi-class classifiers that do not scale well to online detection of large number of events. This paper proposes Sequential Max-Margin Event Detectors (SMMED) to efficiently detect an event in the presence of a large number of event classes. SMMED sequentially discards classes until only one class is identified as the detected class. This approach has two main benefits w.r.t. standard approaches: (1) It provides an efficient solution for early detection of events in the presence of large number of classes, and (2) it is computationally efficient because only a subset of likely classes are evaluated. The benefits of SMMED in comparison with existing approaches is illustrated in three databases using different modalities: MSRDaliy Activity (3D depth videos), UCF101 (RGB videos) and the CMU-Multi-Modal Action Detection (MAD) database (depth, RGB and skeleton). The CMU-MAD was recorded to target the problem of event detection (not classification), and the data and labels are available at http://humansensing.cs.cmu.edu/mad/ .