to download project abstract


Image Classification and detecting objects through machine learning is one of the
advancements in modern technology. Pedestrian Detection is also one of those extended
applications. However, there are many flaws in the existing systems of pedestrian
detection. An algorithim we proposed will benefit the flaws in existing systems. Where
we can detect the pedestrians using our system without a single flaw.
In our system we use convolutional neural network (CNN) for image classification
and R-CNN (Regional CNN) for object detection and IOU helps to measure the accuracy
of algorithms used for object detection using bounding boxes. Thus output is validated by
detecting the pedestrians exactly. This training cascade classifier improves the machine
to overcome the false detections near pedestrians. Experimental results on two widely
used pedestrian datasets demonstrate that the proposed training strategy and the CNN
based detector can effectively improve the detection rate and the localization accuracy
using fewer parameters.

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