1. TRECVID Evaluations Mei-Chen Yeh 05/25/2010

2. Introduction Text REtrieval Conference (TREC) – Organized by National Institute of Standards (NIST) – Support from interested government agencies – Annual evaluation (NOT a competition) – Different “tracks” over the years, e.g. web retrieval, email spam filtering, question answering, routing, spoken documents, OCR, video (standalone conference from 2001) TREC Video Retrieval Evaluation (TRECVID)

3. Introduction Objectives of TRECVID – Promote progress in content-based analysis and retrieval from digital videos – Provide open, metrics-based evaluation – Model real world situations

4. Introduction Evaluation is driven by participants The collection is fixed, available in the spring – 50% data used for development, 50% for testing Test queries available in July, 1 month to submission More details: – http:://www-nlpir.nist.gov/projects/trecvid

5. TRECVID Collections Test data – Broadcast news – TV programs – Surveillance videos – Video rushes provided by BBC (before 2006) – Documentary and educational materials supplied by the Netherlands Institute for Sound and Vision (2007-2009) – The Gatwick airport surveillance videos provided by the UK Home Office (2009) – Web videos (2010) Languages – English – Arabic – Chinese

6. Collection History

7. Tasks Semantic indexing (SIN) Known-item search (KIS) Content-based copy detection (CCD) Surveillance event detection (SED) Instance search (INS) Event detection in Internet multimedia (MED)

8. Semantic indexing System task: – Given the test collection, master shot reference, and feature definitions, return for each feature a list of at most 2000 shot IDs from the test collection ranked according to the possibility of detecting the feature. 130 features (2010) – Full list Full list

9. Examples (1) Shots depicting an animal (no humans) Pictures of flowers Exterior shots of a shopping mall Images of people who appear to be scientists One or more male children Teenagers Shots depicting a crowd …

10. Examples (2) One or more people running One or more people walking One or more people swimming One or more people singing One or more people playing basketball A person throwing some object A person riding a bicycle Putting food or drink in his/her mouth …

11. Known-item search Models the situation in which someone knows of a video, has seen it before, believes it is contained in a collection, but doesn't know where to look. Inputs – A text-only description of the video desired – A test collection of videos Outputs – Top ranked videos (automatic or interactive mode)

12. Examples Find the video with the guy talking about how it just keeps raining. Find the video with the guy talking about how it just keeps raining. Find the video about some guys in their apartment talking about some cleaning schedule. Find the video about some guys in their apartment talking about some cleaning schedule. Find the video with the boy in a red cap throwing a baseball over and over again. Find the video with the guy in a yellow T-shirt with the big letter M on it. … http://www-nlpir.nist.gov/projects/tv2010/ki.examples.html

13. Content-based copy detection

15. Surveillance event detection Detects human behaviors in vast amounts surveillance video, real time! For public safety and security Event examples – Person runs – Cell to ear – Object put – People meet – Embrace – Pointing – …

16. Instance search Finds video segments of a certain specific person, object, or place, given a visual example.

17. Event detection in Internet multimedia System task – Given a collection of test videos and a list of test events, indicate whether each of the test events is present (and the strength of evidence) in each of the test videos In 2010 this task will be treated as exploratory! – Emphases on supporting initial exploration of the new video collection, task definition, evaluation framework, and a variety of technical approaches to the system task – Not much information available so far

18. Call for partners Schedule (July-September)

19. Conclusions Standardized evaluations and comparisons Test on large collections Failures are not embarrassing, and can be presented at the TRECVID workshop! Anyone can participate! – A “priceless” resource for researches