• Shyamala Devi .J
• Selvanayaki Kolandapalayam Shanmugam
• A.Revathi

In today’s world, the important part of research in video processing and in real time applications are the idea of detecting and classifying the objects with a particular feature in the moving video. Technically, image processing is defined as the form of signal processing. There are many existing approaches which are used for tracking objects. Edge matching, Divide-and-Conquer search, Gradient matching, Histograms of receptive field responses, Pose clustering, SIFT, SURF, etc are some of the approaches applied. All these methods are either Appearance based methods or Feature based methods. They lag in one or the other way when it comes to real time applications. So there has been a need for creating a new system that could combine positive aspects of both the methods and increase the efficiency in tracking objects, when it comes to real life scenario. The proposed work is a study to detect and classify the green vegetables in the video by combining the features of SVM and Neural networking algorithms. It consists of three modules, first modules does feature and key point extraction, second module does dimensionality reduction using PCA. The output of the second module is passed into the SVM classifier to classify the vegetables based on its color.

Detection; Classification; Surveillance; Tracking; Extraction; Reduction; Video Processing

"A STUDY OF DETECTION AND CLASSIFICATION OF COLOR FEATURES FOR VEGETABLES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.19-21, March-2019, Available :http://www.jetir.org/papers/JETIRAQ06004.pdf

"A STUDY OF DETECTION AND CLASSIFICATION OF COLOR FEATURES FOR VEGETABLES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp19-21, March-2019, Available at : http://www.jetir.org/papers/JETIRAQ06004.pdf