VENUS: Video Exploitation and Novelty Understanding in Streams

1. VENUS: Video Exploitation and Novelty Understanding in Streams Laboratory for Applied Computing Rochester Institute of Technology Faculty: Dr. Roger S. Gaborski (rsg@cs.rit.edu) Dr. Ankur M. Teredesai Students: Vishal S. Vaingankar, Vineet Chaoji, Aleksey Tentler November 5, 2003

2. Motion Novelty Detection Results Motion Detection map: Detects motion in video sequences. Motion Novelty Map: Detects novel motion from the above motion maps. Motion Detection Map Motion Novelty Map Results Analysis: During the initial frames while the system is still learning motion in different directions, the motion map and the novelty map show similar information. That is, every motion in the motion map is shown as novel in the novelty map. But overtime, when the system has learnt the repeated motion, the novelty map stops firing the person’s walk as novel. Thus after learning, the novelty map reduces the information it detects as novel, compared to the motion map. The system fires novelty if a never-seen-before motion is encountered again. Click on the image to play video

3. Total Novelty Detection Results Motion Detection map: Detects motion in video sequences. Total Novelty map: Detects still and motion novelty in the scene. Motion Detection Map Total Novelty Map Results Analysis: Total novelty map combines the novelty detected in both motion and still domain. Example of Still Novelty: Person places the bag on the small table and leaves, which is detected as novelty since the bag was not part of the original scene. Overtime the position of the bag is learnt and stops being novel. Then the person picks up the bag and leaves which is again a novelty. Example of Motion novelty: System initially fires novelty for person’s motion. But overtime stops detecting the person’s walk as a novel event. Click on the image to play video