A novel framework is introduced to handle the pedestrian detection in mid-high crowd density. This framework exploits the samples obtained with the unsupervised approach, extracts the combined pattern of Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) for the training and detection. The motion information is utilized to adjust the confidence score of detect annotation, and the soft-NMS is used to handle the balance between the removing of redundant annotation and occlusion. The experiments indicate the proposed approach achieved a promising result compared to state-of-the-art trained with the benchmarking dataset.
|Conference||25th IEEE International Conference on Automation and Computing|
|Abbreviated title||ICAC 2019|
|Period||5/09/19 → 7/09/19|
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- Department of Computer Science - Senior Lecturer
- School of Computing and Engineering
- CVIC - Centre for Visual and Immersive Computing - Centre Member