A Competent Frame Work for Efficient Object Detection, Tracking and Classification

Observation is the rising idea in the present innovation, as it assumes a key part in checking sharp exercises at the niches and corner of the world. Among which moving Object distinguishing and following by methods for PC vision systems is the significant part in reconnaissance. On the off chance that we consider moving object recognition in video investigation is the underlying stride among the different PC applications In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification in weka. There are three classifiers such as J48, k-nearest neighbor and Multilayer perceptron are used. The performance of the proposed technique is measured through evaluation phase and is tabulated.