Name
Abstract | ||
|---|---|---|
We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px6352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1371/journal.pcbi.1002591 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
frames per second | Computer vision,Computer graphics (images),Computer science,Haptic perception,Whiskers,Animal behavior,Software,Frame rate,Artificial intelligence,Whisking in animals | Journal |
Volume | Issue | ISSN |
8 | 7 | 1553-7358 |
Citations | PageRank | References |
7 | 0.77 | 2 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan G. Clack | 1 | 7 | 0.77 |
| Daniel H. O'Connor | 2 | 7 | 0.77 |
| Daniel Huber | 3 | 7 | 0.77 |
| Leopoldo Petreanu | 4 | 7 | 0.77 |
| Andrew Hires | 5 | 7 | 1.11 |
| Simon Peron | 6 | 7 | 0.77 |
| Karel Svoboda | 7 | 92 | 10.18 |
| Eugene Myers | 8 | 3164 | 496.92 |