Design and Implementation of Portable Smart Wireless Pedestrian Crossing Control System
Product Description
Aim:
Synopsis:
Pedestrian detection has made significant advances benefiting from the region-based convolutional neural networks (R-CNN). The project introduces a strict matching metric, which is beneficial to selecting well-aligned positive samples A positive sample matches a ground truth only if all its region-IoUs are bigger than a threshold. Secondly, an improved negative example selection strategy using both the classification and localization information is proposed to mine hard negative examples, which can further suppress the false positive detections near the pedestrians.
Proposed System
A cascade compact convolutional neural network (CC-CNN)
is proposed for accurate pedestrian detection. CC-CNN based detector can
effectively improve the detection rate and the localization accuracy using
fewer parameters.
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