Abstract: With the ubiquitous presence of inexpensive video cameras, novel approaches to video-based pattern recognition problems are emerging. Video-based pattern recognition problems have applications in homeland security, healthcare, battlefield awareness, video indexing and anomaly detection. The single most important feature that distinguishes video-based pattern recognition problems from still-image based recognition problems is the motion of patterns and/or sensor. In this talk, statistical classifiers for many video-based recognition problems such as human identification/verification using face and gait features, vehicle identification across non-overlapping cameras and human activity recognition will be presented with examples. A method for compensating for the variations in the rate at which patterns evolve will then be presented. A non-parametric method based on a gait signature will be described for recognizing human motion patterns, with applications to detecting human activities and concealed object detection. Finally, the problem of anomaly detection in video using statistical and syntactic approaches will be described. The talk will conclude with a brief discussion of many theoretical issues and practical problems that remain to be addressed in this area.