Eran Swears

Eran Swears

Staff R&D Engineer

Dr. Swears joined Kitware, Inc. as one of the first Research & Development Engineers in the Computer vision group in 2007. Since then, he has worked as the principle researcher or project lead on several DARPA and AFRL efforts. These projects aligned well with his interests, which include: pattern recognition, machine learning, graphical models, and in particular activity modeling and detection. Dr. Swears has performed research in motion pattern learning, scene decomposition, anomaly detection, tracking, event and activity detection, as well as video and chat text fusion using surveillance video, ground based sensors, and chat text of video.

Prior to working at Kitware, Dr. Swears joined the Discrimination Group at Lockheed Martin in Moorestown, NJ where he designed and developed algorithms for the Aegis system inter-continental ballistic missile tracker. His research included the development of multi-object tracking, track linking, genealogy, and event detection using solid state radar detections. After progressing to the Tracker Team Lead in 2005 he joined the GE Global Research Center as a contractor in 2006. While at GE Dr. Swears researched and developed algorithms for motion pattern learning and anomaly detection with computer vision applications.

Dr. Swears received his Ph.D. from Rensselaer Polytechnic Institute in 2015, an M.S. from Drexel University in Philadelphia PA in 2005, and a B.S. from Rensselaer Polytechnic Institute in 2001. The majority of his graduate studies were performed while working full-time at Lockheed Martin and Kitware. Dr. Swears has numerous publications that include papers in top tier conferences and journals.

  1. A. Hoogs et al., "An end-to-end system for content-based video retrieval using behavior, actions, and appearance with interactive query refinement," in IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS), Karlsruhe, Germany, 2015.
  2. E. Swears, A. Basharat, A. Hoogs, and E. Blasch, "Probabilistic sub-graph matching (pgram) for video and text fusion," in National Symposium on Sensor and Data Fusion (NSSDF), 2014.
  3. E. Swears, A. Hoogs, and K. Boyer, "Pyramid Coding for Functional Scene Element Recognition in Video Scenes," presented at the CVPR Workshop on Scene Understanding, 2014.
  4. E. Swears, A. Hoogs, Q. Ji, and K. Boyer, "Complex Activity Recognition Using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014.
  5. E. Swears, A. Hoogs, and K. Boyer, "Pyramid Coding for Functional Scene Element Recognition in Video Scenes," in International Conference on Computer Vision (ICCV), Sydney, Australia, 2013.
  6. Yongmian Zhang, Yifan Zhang, E. Swears, N. Larios, Ziheng Wang, and Qiang Ji, "Modeling Temporal Interactions with Interval Temporal Bayesian Networks for Complex Activity Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 10, pp. 2468-2483, Oct. 2013.
  7. E. Swears, A. Hoogs, and R. Blue, "Recognizing Activity Based Scene Elements in Video," in Automatic Target Recognition Working Group, 2013.
  8. E. Swears and A. Hoogs, "Learning and recognizing complex multi-agent activities with applications to american football plays," in IEEE Workshop on the Applications of Computer Vision (WACV), Breckenridge, CO, USA, 2012.
  9. E. Swears, M. Turek, R. Collins, A. Perera, and A. Hoogs, "Automatic activity profile generation from detected functional regions for video scene analysis," in Video Analytics for Business Intelligence. Springer Berlin Heidelberg, 2012, pp. 241-269.
  10. S. Oh et al., "A large-scale benchmark dataset for event recognition in surveillance video," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, 2011.
  11. E. Swears and A. Hoogs, "Complex Activity Recognition using Granger Constrained Dynamic Bayesian Network," in Learning Workshop, Fort Lauderdale Florida, 2011.
  12. E. Swears and A. Hoogs, "Functional scene element recognition for video scene analysis," in IEEE Workshop on Motion and Video Computing (WMVC), Snowbird, UT, USA, 2009.
  13. E. Swears, A. Hoogs, and A. Perera, "Learning Motion Patterns in Surveillance Video using HMM Clustering," in IEEE Workshop on Motion and Video Computing (WMVC), Copper Mountain, CO, USA, 2008.