Anthony Hoogs

Learn More
We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one(More)
A fundamental requirement for effective automated analysis of object behavior and interactions in video is that each object must be consistently identified over time. This is difficult when the objects are often occluded for long periods: nearly all tracking algorithms will terminate a track with loss of identity on a long gap. The problem is further(More)
We present a novel vanishing point detection algorithm for uncalibrated monocular images of man-made environments. We advance the state-of-the-art by a new model of measurement error in the line segment extraction and minimizing its impact on the vanishing point estimation. Our contribution is twofold: 1) Beyond existing hand-crafted models, we formally(More)
We present a real-time, full-frame, multi-target Wide Area Motion Imagery (WAMI) tracking system that utilizes distributed processing to handle high data rates while maintaining high track quality. The proposed architecture processes the WAMI data as a series of geospatial tiles and implements both process- and thread-level parallelism across multiple(More)
We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001).(More)
We present a new approach for recognizing rare events in aerial video. We use the framework of hidden Markov models (HMMs) to represent the spatio-temporal relations between objects and uncertainty in observations, where the data observables are semantic spatial primitives encoded based on prior knowledge about the events of interest. Events are observed as(More)
We present a novel approach to automatically annotating broadcast video. To manage the enormous variety of objects, events and scenes in video problem domains such as news video, we couple generic image analysis with a semantic database, WordNet, containing huge amounts of real-world information. Object and event recognition are performed by searching(More)
surveillance, sports and other video domains, many scenes involve complex, multi-agent activities where the agents are interacting in a time-varying manner. In surveillance, people form into queues and others groups, and transfer baggage. Team sports involve multiple players acting in a coordinated effort. Our goal is to efficiently model and recognize such(More)
A common difficulty encountered in tracking applications is how to track an object that becomes totally occluded, possibly for a significant period of time. Another problem is how to associate objects, or tracklets, across non-overlapping cameras, or between observations of a moving sensor that switches fields of regard. A third problem is how to update(More)
A semantic database has been extended with visual information to enable video annotation. This paper describes a lexical database, WordNet. We show its limitations with respect to describing visual characteristics, and describe an extension to WordNet that contains specific visual information. Having such a semantic database makes video annotation possible(More)