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Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance
2019
Machine Learning and Knowledge Extraction
This paper presents a semi-supervised faster region-based convolutional neural network (SF-RCNN) approach to detect persons and to classify the load carried by them in video data captured from distances several miles away via high-power lens video cameras. For detection, a set of computationally efficient image processing steps are considered to identify moving areas that may contain a person. These areas are then passed onto a faster RCNN classifier whose convolutional layers consist of
doi:10.3390/make1030044
fatcat:fys65uqzsfcdnafp6cai2i4bhu