The aim of this project is to develop an application which can detect pedestrians effectively. The problem of motion-based object detection can be divided into two parts:
a) Classifying pedestrians and non pedestrians features
a) Detecting pedestrians in each frame
b) Associating the detections corresponding to the same object over time
Tool :This project is based on Machine learning, We can provide image data set of pedestrians and non-pedestrians as an training data to the software tool which will extract important features using Adaboost classifier or SVM etc. and similar combination of strong/important features will be taken out for post-processing. We can use Python or Matlab as a building tool for this system.
Implementation : The Implementation of such a tool depends on two factors – Feature extraction and object detection methods.
So you can use various classifiers available online and also read about basic feature extraction algorithm.
Research : Detecting humans in images is a challenging task owing to their variable appearance. This is a booming research topic which is still going on for surveillance of large crowds in real time applications. Research areas include image processing, artificial Intelligence and machine learning.
IEEE Transaction paper on https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf
Survey Paper: http://www.thesai.org/Downloads/Volume5No10/Paper_7-A_Survey_of_Pedestrian_Detection_in_Video.pdf
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