Modeling interactions of multiple co-occurring objects in a complex activity is becoming increasingly popular in the video domain. The Dynamic Bayesian Network (DBN) has been applied to this problem in the past due to its natural ability to statistically capture complex temporal dependencies. However, standard DBN structure learning algorithms are generatively learned, require manual structure definitions, and/or are computationally complex or restrictive. We propose a novel structure learning solution that fuses the Granger Causality statistic, a direct measure of temporal dependence, with the Adaboost feature selection algorithm to automatically constrain the temporal links of a DBN in a discriminative manner. This approach enables us to completely define the DBN structure prior to parameter learning, which reduces computational complexity in addition to providing a more descriptive structure. We refer to this modeling approach as the Granger Constraints DBN (GCDBN). Our experiments show how the GCDBN outperforms two of the most relevant state-of-the-art graphical models in complex activity classification on handball video data, surveillance data, and synthetic data.

Recognizing functional scene elements in video scenes based on the behaviors of moving objects that interact with them is an emerging problem of interest. Existing approaches have a limited ability to characterize elements such as cross-walks, intersections, and buildings that have low activity, are multi-modal, or have indirect evidence. Our approach recognizes the low activity and multi-model elements (crosswalks/intersections) by introducing a hierarchy of descriptive clusters to form a pyramid of codebooks that is sparse in the number of clusters and dense in content. The incorporation of local behavioral context such as person-enter-building and vehicle-parking nearby enables the detection of elements that do not have direct motion-based evidence, e.g. buildings. These two contributions significantly improve scene element recognition when compared against three state-of-the-art approaches. Results are shown on typical ground level surveillance video and for the first time on the more complex Wide Area Motion Imagery.

The three articles in this special section are selected papers from the IEEE CS Conference on Computer Vision and Pattern Recognition that was held in Anchorage, AL, in June 2008.