Training Cascade Compact CNN With Region-IoU for Accurate Pedestrian Detection

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Aim:

To improve the Pedestrian detection rate and the localization accuracy using Cascade Compact CNN

Existing System:

R-CNN analysis shows that localization error and background error are still the two main errors for Faster R-CNN based detectors. In the following sections, the proposed approach will deal with these two types of errors by introducing a new example selection strategy and a cascade compact CNN. Besides, the annotations of city person’s validation data set are manually re-annotated for fairer comparison of different pedestrian detectors

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