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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sherrah, J. |
| Copyright Year | 2010 |
| Abstract | Detection of pedestrians under occlusion has been addressed previously with body-part-based approaches, in particular using the generalised Hough transform. Tracking is usually addressed by first detecting pedestrians in each frame independently and then tracking the detections over time. This paper presents a novel variation on the generalised Hough approach: tracking is performed first, and detection second. Robust features on a pedestrian are tracked over short time-frames to form tracklets. Not only do tracklets reduce false alarms due to unstable features, but they provide temporal correspondence information in Hough space. Consequently tracking can be posed as optimal path finding in Hough space and efficiently solved using the Viterbi algorithm. The paper also presents an improvement to the random Hough forest training method by using multi-objective optimisation. |
| Starting Page | 314 |
| Ending Page | 319 |
| File Size | 2014957 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424488162 |
| DOI | 10.1109/DICTA.2010.61 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-01 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training feature tracking Uncertainty Viterbi algorithm Detectors Transforms pedestrian tracking generalised Hough transform Feature extraction tracklets Optimization |
| Content Type | Text |
| Resource Type | Article |
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