In cluttered environments, the overhead view is often preferred because looking down can afford better visibility and coverage. However detecting people in this or any other extreme view can be challenging as there are significant variation in a person's appearances depending only on their position in the picture. The Histogram of Oriented Gradient (HOG) algorithm, a standard algorithm for pedestrian detection, does not perform well here, especially where the image quality is poor. We show that with the SCOVIS dataset, on average, 9 false detections occur per image. We propose a new algorithm where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in the noisy images from our industrial assembly line and detects people with a 95% efficiency. The algorithm is demonstrated as part of a simple but effective person tracking by detection system. This incorporates simple motion detection to highlight regions of the image that might contain people. These are then searched with our algorithm. This has been evaluated on a number of SCOVIS sequences and correctly tracks people approximately 99% of the time. By comparison, the exampled algorithms in the OpenCV are less than approximately 50% efficient. Finally, we show our algorithm's potential for generalization across different scenes. We show that a classifier trained on the SCOVIS dataset achieves a detection rate of 96% when applied to new overhead data recorded at Southampton. Using the output from this stage to generate labelled 'true positives' data we train a new model which achieves a detection rate of 98%. Both these results compared favourably with the performance of a model trained with manually labelled images. This achieves a detection rate of greater than 99%.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:595580 |
| Date | January 2014 |
| Creators | Ahmed, Imran |
| Contributors | Carter, John |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/363113/ |
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