Abstract
The method of constructing an image dataset by sampling images from videos with a short interval keeps the information in the video but also brings redundancy and increases the training costs significantly. In this paper, we propose a method to detect human heads with less training cost and higher performance, including: (1) A filtering standard to screen out the useless image in video-based image dataset with almost the same average precision. (2) An effective head detection model with the fusion of shoulder context. We evaluate our method on a human head dataset – HollywoodHeads and achieve reasonably good performance. This result shows that our method is very useful in human head detection task.
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Le, C., Ma, H. (2018). A Method of Detecting Human Head by Eliminating Redundancy in Dataset. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_57
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DOI: https://doi.org/10.1007/978-981-13-1702-6_57
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