EM-Based Mixture Models Applied to Video Event Detection

When it comes to a computer vision system, automatic video event comprehension is a challenging task due to motion clutter, event understanding under complex scenes, multilevel semantic event inference, contextualization of events and views obtained from multiple cameras, unevenness of motion scales, shape changes, occlusions and object interactions among lots of other impairments. In recent years, state-of-the-art models for video event classification and recognition [Zhang et al. (2011), Yacoob et al. (1999)] include modeling events to discern context, detecting incidents with only one camera (Ma et al. (2009), Zhao et al. (2002), Zelnik-Manor et al. (2006)], low-level feature extraction and description, high-level semantic event classification and recognition. Even so, it is still very burdensome to recuperate or label a specific video part relying solely on its content.